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University of Sindh Journal of Information and Communication Technology (USJICT)

Volume 2 Issue 1

January – 2018

Published by Office of Dean Faculty of Natural Sciences and Institute of Information and Communication Technology, University of Sindh., Jamshoro

ISSN-P: 2521-5582 ISSN-E: 2523-1235

Sindh University Press University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)

In this modern era, the scientific research and innovations is taking the front line in academia and amongst the academicians. The role of high quality research journals is heighted to ensure publication and dissemination of these scientific research and innovative ideas. The field of Information Technology, Software Engineering, Computer Science, Electronics and Telecommunication is ever growing and most significant in 21st century. The University of Sindh Journal of Information and Communication Technology (USJICT)has been established to bridge the gap between researchers and dissemination of their research findings amongst the academia. The journal objective is to server as a key resource in disseminating latest trends and technological knowledge in the domain of Information and Communication Technologies. USJICT is an open access, double blind peer reviewed research journal, published quarterly by the office of Dean faculty of Natural Sciences and Institute of Information and Communication Technology with a focused aim on promoting and publishing original high-quality research.

Mission The mission of this journal is to promote innovative ideas, latest trends and original research in the fields of Information Technology, Software Engineering, Computer Science, Electronics and Telecommunication. The core focus of USJICT is concentrated on promoting and propagating novel and innovative research amongst the readers of this journal. The most important criterion for acceptance/rejection is originality of the material presented in the manuscript.

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)

Aim of Journal

The aim of this journal is to encourage researchers, investigators and scientists to publish their research findings allowing wider dissemination of their intellectual knowledge, with aim of applying those for the benefit of the society. The newly launched journal would cover full spectrum of the specialties in Information Technology, Software Engineering, Computer Science, Electronics and Telecommunication. It would include original research articles, review articles, case reports, and scientific findings from within specified domain areas of ICT. The journal strictly follows the guidelines proposed by Higher Education Commission (HEC) Pakistan.

Publication University of Sindh Journal of Information and Communication Technology is published quarterly i.e., 4 times a year: January, April, July and October, by the office of Dean Faculty of Natural Sciences and Institute of Information and Communication Technology, University of Sindh, Jamshoro

Copyright All Rights Reserved. No part of this publication may be produced, translated or stored on a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying and/or otherwise the prior permission of publication authorities.

Copyright © University of Sindh, Jamshoro. 2017 All Rights Reserved.

Printed at: Sindh University Press. .

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)

Open Access Policy

University of Sindh Journal of Information and Communication Technology provides an immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. To enable unrestricted usage our Journal is following CC-BY-ND license. Researchers can copy and redistribute the material in any medium or format, for research purpose, condition to formal reference to the original work. Authors can self-archive publisher's version of the accepted article in digital repositories and archives.

Contact Information

Dr. Zeeshan Bhatti Assistant Professor & Editor Journal Office, Institute of Information and Communication Technology University of Sindh, Jamshoro, Sindh, Pakistan

Journal Website: http://sujo.usindh.edu.pk/index.php/USJICT/ e-mail: [email protected] [email protected] Contact: +92 333 2606630

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018) Editorial Team

Patron Dr. Fateh Muhammad Burfat Vice Chancellor, University of Sindh, Jamshoro

Editor in Chief Prof. Dr. Akhtar Mughal Dean Faculty of Natural Sciences, University of Sindh

Editor

Dr. Zeeshan Bhatti Institute of Information and Communication Technology, University of Sindh

Co-Editor(s)

Prof. Dr. Imdad Ali Ismaili Director, Institute of Information and Communication Technology, University of Sindh

Prof. Dr. Lachhman Das Dhomeja, Institute of Information and Communication Technology, University of Sindh

Associate Editor(s)

Prof. Dr. Khalil-ur-Rehman Khoumbati Dr. Azhar Ali Shah University of Sindh University of Sindh

Dr. Dil Nawaz Hakro Dr. Abdul Waheed Mahesar University of Sindh University of Sindh

Technical Editor(s)

Dr. Shahzad Memon Dr. Kamran Taj Phathan University of Sindh University of Sindh

Dr. Niaz Hussain Arijo Dr. Muhammad Ali University of Sindh University of Sindh

Journal Manager Comp. Operator / Assistant Journal Manager Dr. Kamran Brohi Mrs. Afia Bhutto University of Sindh University of Sindh

Graphics Designer and Publication Manager Editorial Assistant Dr. Zeeshan Bhatti Sayed Mehran University of Sindh Faiza Parveen Abid Ali Pathan

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)

National Editorial Advisory Board

Dr. Bhawani Shankar Chowdhry Faculty of Electrical, Electronics, & Computer Engineering Mehran University of Engineering & Technology, Jamshoro

Dr Aqil Burney Computer Science and Actuarial Sciences, Institute of Business management, Karachi

Dr. Nadeem Mahmood Department of Computer Science, University of Karachi, Pakistan

Dr. Farhan Ahmed Siddiqui Department of computer Science, University of Karachi, Pakistan

Dr Tahseen Jillani Department of Computer Science, University of Karachi

Dr. Fida Hussain Chandio Institute of Mathematics and Computer Science (IMCS), University of Sindh, Pakistan

Dr. Yasir Arfat Malkani Institute of Mathematics and Computer Science (IMCS), University of Sindh, Pakistan

Dr. Kamran Ahsan Department of Computer Science, Federal Urdu University of Science Arts and Technology, Karachi

Dr. Zahid Hussain Abro Faculty of Science, Quaid-e-Awam University of Engineering, Science and Technology (QUEST), Nawabshah

Dr. Mukhtiar Ali Unar Department of Computer Systems Engineering, Mehran University of Engineering and Technology (MUET), Jamshoro

Dr. Mohammad Altaf Mukati Dean, Computing & Engineering Sciences, SZABIST Karachi,

Dr Salman Abdul Ghafoor Department of Electrical Engineering, National University of Sciences and Technology (NUST) Islamabad

Dr. Waseem Shahzad Department of Computer Science, FAST National University, Islamabad

Dr. Muhammad Azhar Naeem Department of Electrical Engineering, Punjab University, Lahore

Dr. Kamran Abid Department of Electrical Engineering, Punjab University, Lahore

Dr. Shahid Iqbal Electrical Engineering, COMSATS, Attock Campus.

Dr. Adnan Akhunzada Institute of Information Technology, COMSATS, Islamabad

Dr. Ahmed Waqas Department of Computer Science, IBA University Sukkur

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)

Dr. Adnan Abid Department of Computer Science, University of Management & Technology

Dr. Huma Javed Department of Computer Science, University of Peshawar

Dr. Kamran Raza IQRA University Karachi.

Dr. Asim ur Rehman Khan National University, FAST, Karachi campus

Dr. Mushtaq Ali IT Department, Hazara University

Dr. Samina Rajper Department of Computer Science, Shah Abdul Latif University, Khairpur

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018) International Editorial Advisory Board

Dr. Asadullah Shah Khulliyah of Information and Communication Technology, International Islamic University, Malaysia

Dr. Uffe Kock Wiil Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU)

Dr. Abdul Hafeez Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia

Dr. Adamu Abu Baker Khulliyah of Information and Communication Technology, International Islamic University, Malaysia

Dr. Mueen Uddin Department of Information Systems, Effat University Jeddah Saudi Arabia

Dr. Zulkefli Muhammad Department of Information Systems, International Islamic University of Malaysia

Dr. Hafiz Abid Mahmood Malik College of Computer Studies, AMA International University, Bahrain

Dr. Asadullah Shaikh College of Computer Science, Najran University, Saudi Arabia

Dr. Ajith Abraham Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, USA

Dr. Khurram Shahzad College of Information Science and Electronics Engineering, Zhejiang University, China

Dr. Loo Chu Kiong Department of Artificial Intelligence, University of Malaya, Malaysia

Dr. Yogesh K. Dwivedi Swansea University, Swansea, Wales, United Kingdom

Dr. Mustafa Ali Abuzaraida Faculty of Information Technology. Misurata University. Misurata – Libya

Dr. Imran Memon Zhejiang University, China

Dr. D. M. Akbar Hussain Department of Energy Technology, Aalborg University Denmark.

Dr. Ayaz Hakro College of Information Science and Electronic Engineering, Zhejiang University, China

Dr. Mohamed Ridza Wahiddin Department of Computer Science, International Islamic University of Malaysia

Dr. Bernard Archimede Professer des Universités, Ecole Nationale d'Ingénieurs de Tarbes, France

Dr. Noemi Scarpato Telematic University “Università Telematica San Raffaele Roma” , Rome, Italy

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018) Editorial Reviewer Board

Dr. Adamu Abu Baker Khulliyah of Information and Communication Technology, International Islamic University, Malaysia

Dr. Hafiz Abid Mahmood College of Computer Studies, AMA International Malik University, Bahrain

Mr. Ghullam Mustafa Shoro Department of Energy Technology, Aalborg University, Denmark

Dr. Bisharat Rasool Memon University of Southern, Denmark

Dr. Mohammad Izzuddin Department of Computer Science, International Islamic University Malaysia

Dr. Mustafa Ali Abuzaraida Faculty of Information Technology. Misurata University. Misurata, Libya

Dr. Imran Memon Zhejiang University, China

Dr. Abdul Rehman Gillal University Technology Petronas, Malaysia

Dr. Fida Hussain Chandio Institute of Mathematics and Computer Science (IMCS), University of Sindh, Pakistan

Dr. Yasir Arfat Malkani Institute of Mathematics and Computer Science (IMCS), University of Sindh, Pakistan

Dr. Ahmed Waqas Department of Computer Science, IBA University Sukkur

Dr. Huma Javed Department of Computer Science, University of Peshawar, Pakistan

Dr. Samina Rajper Department of Computer Science, Shah Abdul Latif University Khairpur, Pakistan

Dr. Yaqoob Koondhar TandoJam University, Sindh, Pakistan

Dr. Muhammad Malook Rind Sindh Madarsatul Islam University, Karachi, Pakistan

Engr. Dr. Faheem yar Khuwar Mehran University of Engineering and Technology, Pakistan

Engr. Majid Hussain Memon Quaid-E-Awam University of Engineering, Science & Technology (Quest), Nawabshah,

Dr. Kamran Khowaja Isra University, Hyderabad, Pakistan

Dr. MunebaMemon Department of Computer Science, Indus University, Karachi, Pakistan

Dr. Safiullah Soomro College of Computer Studies, AMA International University, Bahrain

Mr. Nasratullah Khan Yanbu University, Department of Computer Science and Engineering, Kingdom of Saudi Arabia

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)

Mr. Burhan Saleh Department of Computer Engineering, Çukurova University, Turkey

Dr. Sharyar Wani National Defence University of Malaysia, Malaysia

Dr. Atia Bano University of Sindh, Laar, Campus, Pakistan

Mr. Aamir Hussain University Technology Petronas, Malaysia

Dr. Muna Azuddin Department of Computer Science, International Islamic University Malaysia

Dr Asif Ali Shaikh Mehran University of engineering and technology, Jamshoro, Pakistan

Dr. Rajab Ali Malookani Department of Mathematics and statistics, QUEST, Nawabshah, Pakistan

Dr. Sufyan Salim Aldabbagh Department of Computer Science, University of Mosul, Iraq

Naeem Atanda Balogun1 Department of Information and Communication Science, University of Ilorin, Nigeria

Dr. Imtiaz Ali Halepoto Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah

Dr Nadeem Qaisar Mehmood University of Lahore Gujrat Campus

Dr. Muhammad Asif Iqbal University of Sargodha Gujranwla Campus

Dr. Pardeep Kumar Department of Computer System Engineering, QUEST Nawabshah, Pakistan

Dr. Amjad Ali Kambho Institution of Engineering and Technology (IET), Hamdard University, Karachi

Ms. Mehwish Naeem Isra University, Hyderabad, Pakistan

Dr. Muawain Abdul Kerim Yasar University, Turkey

Dr. Mostafa Karbasi Department of Computer Science, International Islamic University Malaysia

Dr. Aqeel ur Rehman Department of Computing, Hamdard University, Karachi

Dr. Jinrui Guan Department of Mathematics, Taiyuan Normal University, China

Dr. Intesab Hussain Sadhayo Quaid-e-Awam University of Engineering Science and Technology (QUEST), Nawabshah

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018) Scope of Journal

The primary scope of the journal is from the disciplines of Information Technology, Software Engineering, Computers Science, Electronics and Telecommunication. The specific thematic areas of interest comprising, but are not restricted to, the following: Information Technology, Software Engineering and Computer Science

 Information systems  System Software  Artificial Intelligence and Pattern Recognition  Computer Architecture  Design and Analysis of Algorithms,  Computer Vision and Image Processing  Expert systems  Embedded and Real-Time Systems  machine learning  System & Software Engineering  Pattern Recognition  Internet and Web  operating systems  Internet of Things (IOT)  Distributed Systems  Computer Networks and Distributed Computing  Genomics & Bioinformatics  Data communication & networks  Web technologies,  human-computer interaction  Database Systems  Cloud Computing  Data Science  Theory and Algorithms  Data Management and Data Mining  Software Testing & Quality assurance  Knowledge Engineering  Systems Engineering  Social Computing  Mobile and Pervasive Computing  Soft Computing Systems  technology enhanced learning,  Network Science  Multimedia Technology  Computer and Network Security  Computer Graphics  Computing Languages & Algorithms  Virtual and Augmented Reality  Software Systems  Game Development  Information Security  High Performance Computing,  theoretical computer science  Software Engineering Tools and Methods  Software Lifecycle  Software Management, Engineering Process  Biometric authentication and algorithms  Combinatorics, Graph Theory, and Analysis of Algorithms

Electronics  Control Systems & Engineering  Transmission and Distribution  Computer Architecture and Systems  Power Electronics  Robotics and automation1  Renewable Energy  Industrial Electronics  Electrical Engineering Materials  Electric Power Generation  High Voltage Insulation Technologies  Lightning Detection and Protection  Power System Analysis  Electrical Measurements  Control and Computer Systems  Complex Adaptive Systems  Microelectronic System  Electronic Materials  Design and Implementation of Application Specific Integrated Circuits (ASIC),  System-on-a-Chip (SoC)  Electronic Instrumentation Using CAD Tools.  VLSI Design-Network Traffic Modelling,  Green Energy  SCADA  Acoustics, speech & signal processing  Circuits and systems  Semiconductors  Control systems  Surface sensing technologies

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)  Electrical machinery 

Telecommunication  Mobile and personal communication systems  Modulation and Signal Processing for Telecommunication,  Computer Networks & Communications  wireless and Mobile Communications,  Satellite communication  Information Theory and Coding  Communication system  Communication Electronics and Microwave  Antenna and Wave Propagation  Radar Imaging,  Distributed Platform  Telematics Services,  Security Network,  Radio Communication.  3G/4G/5G Network Evolutions  CDMA/GSM Communication Protocols  Wireless Access Security  telephone, telegraphy, facsimile,  electromagnetic propagation including radio;  telecommunication error detection and wire; aerial; underground, coaxial, and correction submarine cables;  multiplexing and carrier techniques;  communication switching systems  Modulation, signal design, and detection  Precoding, equalization, and synchronization  Source and channel coding  MIMO systems  Millimeter wave communication  Relay-based communication  Smart Grid Communications  Physical layer security  Fiber-based and wireless optical  Nano- and molecular communications communication  Heterogeneous networks  Green and energy harvesting communications

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Review Policy and Process

University of Sindh Journal of Information and Communication Technology strictly follows a full double-blind peer review process, comprising the following steps:

 All papers submitted to the USJICT are first examined by an Editor for the initial review. The editor may assign an Associate Editors to review the quality and scope of the submitted manuscript. If the article fails to meet the journal criteria, the paper is rejected immediately and authors are notified.

 Once the paper is submitted, the editor or Associate editor ensures that the paper follows the double-blind review policy. If the author names are found, then the author name(s) and affiliation(s) are removed from the paper and updated version is uploaded on the system either by the editor or corresponding author.

 Every submitted article is checked for the Similarity report by the Editor, before forwarding it to the reviewers. Turnitin system is used by the journal as prescribed by Higher Education Commission (HEC) Pakistan, to check the similarity of paper. As per HEC policy, in case manuscript has been found to have a similarity index of more than 19% it will be immediately either returned back to the author for correction and resubmission, or will be rejected and archived. This decision is made by the editor, based on the similarity ratio. (Please Note that the parameters for similarity check involve, Add to No Repository, Exclude Bibliography, Exclude Quoted Text).

 If the paper satisfies the criteria, then the editor or associate editor will send the article to at least (03) reviewers for review. Each article will be reviewed by at least 01 National and 01 International reviewers with a double-blind, peer review policy.

 Reviewers are recruited from the national and international universities having a PhD with reputed research profile (checked through Google Scholar and Research Gate) for the given area/ field of submitted article.

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 Each reviewer is initially invited to review the article showing them the article title and abstract only. Upon acceptance of the review request the full paper is presented and a review form is used to record their feedback and suggestions through online management system.

 The reviewers' recommendations will determine whether to “Accept Submission”, “Revision Required”, “Resubmit for Review” or “Decline Submission”. The final decision if made by the Editor based on the reviewers’ report.

 Once the decision has been made, the review response - including the review form and suggestion, are sent to all authors of the article with editorial decision. The same email is then forwarded (BCC) to all reviewers (with Blind policy) and Associate Editors corresponding the article for ensuring transparency and high standards.

 For the papers which require only revisions, the Associate or Technical editor will re- review the paper for ensuring that the reviewer’s suggestions have been incorporated or not.

 For papers that require resubmission for review, will undergo second Round of review by the same or different reviewers to ensure that the quality of the revised paper is acceptable.

 After final Acceptance, the Author/Corresponding author will be notified and paper will be forwarded to technical editors for copy-editing, layout editing and proof reading.

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January, 2018)

Table of Contents S.No Title and Author Name Page No.

1. A Survey of Data Mining Techniques for Crime Detection 1 – 6 Shamaila Qayyum, Hafsa Shareef Dar

2. Security of Provider sides in Data Privacy and Data Accessibility 7 – 10 Issues in Cloud computing Taimoor Ahamd, Hala Aslam, Shahzeb Shahzad

3. Modification of Heun’s Iterative Method for the Population Growth 11 – 16 Rate Problems Aliya Pirzada, Asif Ali Shaikh, Syed Feroz Shah

4. Agricultural Environmental Monitoring: A WSN Perspective 17 – 24 Mushtaque Ahmed Rahu, Pardeep Kumar, Sarang Karim, Azeem Ayaz Mirani

5. Software Outsourcing Cost Estimation Model (SOCEM). A 25 – 30 Systematic Literature Review Protocol Jamshed Ahmad, Abdul Wahid Khan, Iqbal Qasim

6. Modified Linear Convergence Mean Methods for Solving Non-Linear 31 – 35 Equations Umair Khalid Qureshi, Zubair Ahmed kalhoro Asif Ali Shaikh, Zohaib Ali Qureshi

7. Correlation of Online Risks and Harm among Teenagers in 36 – 43 Bangladesh Taslim Taher, Mohd Adam Suhaimi

8. Earthquake Monitoring & Early Warning System 44 – 51 Zaryab Qazi, Mubashir Malik, Waseem Javaid Soomro

9. Assessing ICT Implementation and Acceptance at Public Sector 52 – 56 Universities in Pakistan Shahmurad Chandio, Muhammad Sadry Abu Seman, Suhaila Samsuri, Abida Kanwal, Asadullah Shah

10. Estimation of Absolute Speed of Vehicle with the Simplified Inverse 57 – 60 Model Seher Zamir, Syeda Sumbul Zehra Naqvi, Syeda Hira Fatima

11. Analysis of Device-to-Device Communication System in the Presence 61 – 67 of Multiple Co-Channel Interference. Zakir Hussain, Asim ur Rehman Khan, Haider Mehdi, Syed Muhammad Atif Saleem, Muhammad Asad Khan

12. Development of an Arduino Based Device for Early Detection of Gas 68 – 72 Leakage Sehreen Moorat, Hiba Pervaiz, Faryal Soomro, Maham Mahnoor

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13. Offline Signature Recognition and Verification System Using 73 – 80 Artificial Neural Network Aqeel-ur-Rehman1, Sadiq Ur Rehman2, Zahid Hussain Babar1, M. Kashif Qadeer1 and Faraz Ali Seelro

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ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/ A Survey of Data Mining Techniques for Crime Detection Shamaila Qayyum, Hafsa Shareef Dar Department of Software Engineering, International Islamic University, Islamabad [email protected], [email protected]

______Abstract: In large datasets, data mining is one of the most powerful ways of knowledge extraction or we can say it is one of the best approaches to detect underlying relationships among data with the help of machine learning and artificial intelligence techniques. Crime Detection is one of the hot topics in data mining where different patterns of criminology are identified. It includes variety of steps, starting from identification of crime characterization till detection of crime pattern. For this purpose, various crime detection techniques have been discussed in literature. In this paper, we have selected widely adapted data mining techniques that are specifically used for crime detection. The analytical study is presented with an extraction in form of strengths and weakness of each technique. Each technique is specific to its use. This survey would serve as a helping guide to researchers to get state of the art crime detection techniques in data mining along with pros and cons.

Keywords: Data mining, Crime detection, Classification, Clustering, Association, Prediction, Constraint, Association rules ______

I. INTRODUCTION through computers is more convenient and less costly than With the advancement of technology, criminals are also hiring and training people for the purpose of collecting and adapting smarter ways to commit crimes. With time, the analyzing existing crime information. Data Mining can crime rate has been tremendously increased instead of help the investigators, no matter if they are experienced or decreasing. The technology where, has helped the people not, to explore large databases efficiently[10]. This not in making their lives easier, has also helped the criminals only helps them in tracking and solving crimes but also in making new plans for their crimes. It is becoming trivial predicting crimes in advance[11]. Data mining techniques to get smarter and technical ways to investigate and prevent offers some predictive models that manipulate the hidden these crimes. One of the most common ways of crime that information and can predict the trends [12]. can be seen nowadays, is not just within the streets, but in Hosseinkhani et al.,[13] has suggested some data mining the world of connectivity; the internet. It is deemed techniques that can be used for crime detection. These important to discover and adapt the ways that help in techniques are clustering, association rule mining, efficient discovery and prevention of such crimes. The deviation detection, classification and string comparator. criminal acts range from the street crimes to massive terror The crime detection data mining techniques as presented attacks and undoubtedly to offend large databases and by Hsinchun et al., are entity extraction, clustering, systems. This all lie under the umbrella of crime[1]. Crimes association rule mining, sequential pattern mining, can be defined for example as network attacks [2], Hacking deviation detection, classification, string comparator and [3], network intrusion [2], cyber fraud [2, 4],deceptive social network analysis [14]. Hossein Hassani et al., have information [5]. later presented a review of some existing crime data mining Data mining refers to the extraction, discovery and analysis techniques which included; entity extraction, cluster of meaningful patterns and rules from a very large amount analysis, association rule, classification and social network of data. It is emerging as very useful tools for crime analysis [15]. The aim of this study is to get an insight of detection [6]. Data mining is a very powerful tool to the techniques that are followed for crime data mining. undermine the activities of criminals by analyzing the Previously, survey on data mining techniques was criminal’s record and information and preventing the conducted with only five techniques that covered small crimes in future[7]. Data mining for crime detection is scope of data mining. In this survey, we intend to cover considered one of the most important research area as each possible technique used for crime detection. described in [8] despite data mining being a new and In this study the existing techniques of data mining for evolving field itself [9]. Data mining is thought of being crime detection and investigation are thoroughly observed. very helpful and accurate in understanding the crime trends Different data mining techniques are discussed and as compared to Humans. Humans are often error prone analyzed for their usage. They are then further compared specially when overworked; computers prove to be more for their strengths and weaknesses under different accurate as compared to human. Mining information University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg.: 1-6 circumstances of usage. The differences and This is very helpful in investigating the simultaneous commonalities of these techniques have also been occurrences of events[27]. The strength of association can discussed. be measured in terms of support; the applicability of rule to given dataset and confidence; the frequency of II. EXISTING CRIME DATA MINING TECHNIQUES: appearance of one data in transactions that contain another Crime can range from the simple street crimes to data[28]. internationally planed crimes[8]. Crime data mining, as compared to usual data mining, is more concerned with D. Sequential pattern mining privacy [16]. In structured data, the patterns are identified Sequential pattern mining discovers frequently through different traditional data mining techniques such occurred sequence of items at different intervals of times. as association, classification, prediction, clustering and It is helpful in network intrusion detection. For meaningful outlier analysis[17]. Advanced data mining handles both results, a large amount of structured data is required[2]. structured and unstructured data for pattern recognition Ayres et al., have provided an algorithm that finds all [14, 18]. In this section we analyze the existing data possible sequences in the transactions data, very quickly. mining techniques that are used for crime detection and They have used depth first traversal combined with the investigation. bitmap representation to achieve this [29].

A. Entity extraction E. Deviation detection It is used to identify persons, vehicles, texts basically It is also known as outlier detection. It studies data that by identifying patterns [19]. It is the process of extracting has clear distinction from rest of the data set. This data from text documents [15]. In computer forensics it can technique is very helpful in fraud detection and other crime help in identifying programs write by hackers. This is done analysis[2, 4]. Aggarwal, in his research has proposed an by grouping similar programs. However it requires a huge evolutionary outlier detection algorithm which works by amount of clean data to produce good results[3]. The main selection then crossover and then mutation methods [30]. approaches for entity extraction are machine learning, Whereas, Arnind et al., have proposed a linear method of statistic based, rule based and lexical lookup [19]. deviation detection in larger database, which provides Machine learning techniques use algorithms to extract solution by simulating a mechanism that is familiar to knowledge and derive patterns e.g, decision trees, neural human beings [31]. networks[20], entropy maximization [21] and hidden markov models [22]. Rule based systems are the structural, F. Classification contextual or lexical hand crafted rules to identify Classification has some predefined classes. This data entities[23]. Statistical based systems used training dataset mining technique finds out some common characteristics to obtain statistics for identifying the occurrence of of different crime entities. These are then organized into particular patterns[24]. Lexical loops systems maintain the predefined classes. A predefined classification scheme popular entities of interest and look up in text the phrases is required for this approach[4]. Classification is very that are specified in their lexicons[21]. helpful in predicting crimes and identifying crime entities with a very less amount of time. However, it needs a B. Clustering techniques predefined scheme for classification and complete training They help in maximizing or minimizing the interclass with testing data, as only this could bring accuracy to the similarities by grouping the data items into the classes predicted results [4]. Classification aims to discover a set based on their characteristics. In criminal investigation, it of rules from the dataset. Classification can be carried out can help in identifying criminals that follow a set pattern either by Decision Trees[32-34], Support Vector Machines for committing a crime [25]. Clustering functions by [35], [35]Naïve Bayes Rules[36] or Neural Networks [37- obtaining the distance measurement among objects, such 42]. as Euclidean distance, Minkowksi distance and Manhattan distance. Different algorithms of cluster group the data into G. String comparator hierarchical manner or partition them as per It computes the similarity among the database records requirements[26]. Apart from these two approaches, other by comparing their textual fields. It helps identifying main approaches for clustering are: density based, grid deceptive information. However it requires a large amount based, model based and constraint based. of computations [5]. It returns a numerical value by comparing two strings [23]. C. Association rule mining It presents the patterns as the rules by finding frequent H. Social network analysis occurrences of items within a dataset. This is helpful in Social network analysis analyses the role and identifying network attacks [2]. It was initially built to interaction of nodes within a conceptual framework. It can observe interesting co-occurrences in market data[23]. be used to identify criminal’s roles by creating a network.

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It can also help in analyzing the flow of information among The mentioned techniques have been deeply studied to these entities, though it won’t help in identifying networks’ know the pros and cons of any technique while adapting it true leaders[5].it reveals the structure within some text, by in crime detection. Entity Extraction is enhanced by presenting some interlinked entities[43]. This shows that machine learning techniques but polluted data can be a people have participated or communicated somewhere[44]. hurdle to it so its weakness is requirement of clean data. The most widely used techniques for SNA are: Degree; The strength of clustering is detection of outliers without number of nodes connected to any node [45], Density; labeled data but as this process is costly and its number of edges in a specific area as compared to the effectiveness depends on selected method as well. overall number of edges[46] and Centrality; the importance Association Rule Mining is yet another technique which of a node within a structure[47]. basically supports classification and its weakness is its specific nature to classification rules only. Sequential Hossein Hassani et al., have reviewed the data mining pattern mining has wide range of applicability in all areas techniques for crime. This review cover the techniques: and hence, large amount of structured data is required for entity extraction, cluster analysis, association rule, its execution. Deviation detection is widely used in fraud classification and social network analysis [15]. In [48] a detection but data dependency in some areas is still a tool was discussed which is based on Natural Language question unsolved. Classification technique is Processing technique for detection of white collar crimes. conventional technique with very less time consumption However, a comparative analysis of all above mentioned and weakness is predefined scheme of classification that crime data mining techniques is still missing in the requires complete training data set. String comparator literature. accuracy is great when we consider numerical values but it requires high computations. Social network analysis III. ANALYSIS focuses on relationships between actors rather than their In this section, comparative analysis of each technique is attribute which makes it more direct but unfortunately it presented on the basis of its strength and weakness. The doesn’t identify network’s true leader in the system. strength and weakness of each technique has been extracted from literature review in which authors and IV. CLASSIFICATION OF EXISTING TECHNIQUES researchers have identified positive and negative impacts In order to understand the techniques used for crime data of particular technique. Table 1 is a complete analysis of mining, we first present classification of these techniques. each technique: This classification contains the data mining techniques that are specifically used for crime data mining and are stated TABLE 1 STRENGTH AND WEAKNESS OF EACH CRIME in the literature. The classification of the techniques along DATA MINING TECHNIQUE with the methods they use is shown in Fig 1. TECHNIQUE STRENGTH WEAKNESS Entity extraction Machine learning large amount of clean makes it easier data required Clustering Detect outliers Computational cost is without any required high. Its effectiveness label data also depends upon the method used Association Rule Support It is used for the most mining accurate classification rules Sequential pattern Wide range of large amount of mining applicability structured data is required Deviation Widely applicable in Sometimes its data detection fraud detection dependency becomes a hurdle Classification Very less time Predefined scheme of consumption classification and complete training dataset required String Accuracy in terms of Large amount of Comparator numerical value computation required Social network focus on relationships Won’t identify analysis between actors rather network’s true leaders than attributes of actors

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Identifying Criminals Social Network [42, 43, 44, 45, 46, role analysis 47]

This classification not only helps researchers but also investigators, to choose the right technique for their scenario, without wasting time. To understand these techniques in more details and to have a comparative analysis of these details, we have identified some attributes of these techniques, after a thorough study of the techniques. These attributes are defined as follow:

i. Timeliness is one of the most important attribute. Most investigators are concerned with the timely response of a data mining technique. ii. Data dependency is another important attributes. At times the techniques require a lot of data to produce accurate results, but the required amount of data may not be available. iii. Accuracy is very important in identifying the true criminals. So this attribute is also very important for analyzing crime data mining techniques.

A. Comparison of Existing Crime Data Mining Techniques Figure 1. Classification of crime data mining techniques This section aims to compare and analyze the existing techniques for crime data mining, on the basis of proposed In order to clearly understand the usage of these thematic taxonomy. We have learnt that there are various techniques, it must be clear to the researchers and techniques used for detection of crime, and every technique investigators that what kind of usage each technique possess. In order to take the full advantage of these is used in different scenario. So the real comparison is techniques and to make their selection easier for the according to the scenario in which they are used, and they may not need to be compared for the common attributes. researchers and investigators, we have thoroughly studied But however, it is important to understand the comparison the techniques that are helpful in crime data mining; we of these techniques for effective selection and flawless have then prepared a comparison of these techniques based investigation. The comparison is based on three values – on their usage. We have identified all the areas in which crime data mining techniques can be used and have further timeliness, data dependency and accuracy of the technique. identified which data mining technique is used in each Table 3 shows the comparison of these techniques based on the proposed taxonomy. scenario. We have thus, further classified these techniques based on their usage. The usage of each crime data mining TABLE 3 COMPARISON OF CRIME DATA MINING technique is presented in Table 2. TECHNIQUES

COMPARISON OF CRIME DATA MINING TECHNIQUES TABLE 2 USAGE BASED CLASSIFICATION OF CRIME DATA MINING TECHNIQUES ATTRIBUTE/TECHNIQ TIMELINES DATA ACCURAC UE S DEPENDENC Y USAGE DATA MINING REFERENCE Y TECHNIQUE Entity extraction Less time Huge Accurate Identification of Entity extraction [11, 15, 16, 17, 18, consumptio amount of programs written by 19, 20, 21] n data required hackers Clustering Moderate Less data Accurate Identifying criminals Clustering [22, 23] Time dependency following a set pattern consumptio Identifying network Association rule [20, 24, 25, 26] n attacks mining Association Rule Moderate Moderate Accurate Intrusion detection Sequential pattern [24, 27] mining mining Sequential pattern Moderate Huge Accurate Fraud detection Deviation detection [24, 28, 29, 30] mining amount of Predicting crimes Classification [28, 31-33, 36-41] data required Identifying deceptive String Comparator [20, 42] Deviation detection Less time Moderate Accurate information consumptio n

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Classification Less time Complete Accurate VI. FUTURE WORK consumptio training set on data n data required availabilit Crime data is an important area in which efficient crime y detection data mining techniques play vital role for analyst String Comparator More time Moderate Accurate and law enforcers to proceed the case in investigations and consumptio n help resolving criminal cases. The scope of this study can Social network Moderate Moderate No true be further enhanced by working on criminal investigation analysis leader data set like FBI and crime detection of counter terrorism identified measures. Another enhancement of this technique is to [2] implement in any integrated ERP software

The comparison presented in table 2 is quite elaborative in REFERENCES terms of attribute’s timeliness, data dependency and [1] P. Kanellis, Digital crime and forensic science in cyberspace: accuracy. Entity extraction is first attribute which has high IGI Global, 2006. data dependency with minimum time consumption and [2] W. Lee, S. J. Stolfo, and K. W. Mok, "A data mining optimal accuracy. Clustering is one of the renowned framework for building intrusion detection models," in Security and techniques in data mining, but for crime detection its time Privacy, 1999. Proceedings of the 1999 IEEE Symposium on, 1999, pp. consumption is moderate having less data dependency and 120-132. [3] A. Gray, S. MacDonell, and P. Sallis, "Software forensics: is accurate. Association rule mining is yet another Extending authorship analysis techniques to computer programs," 1997. technique used for crime detection having moderate [4] O. De Vel, A. Anderson, M. Corney, and G. 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Shoemaker, "A note on least-squares learning procedures and classification by neural network models," IEEE Transactions on Neural Networks, vol. 2, pp. 158-160, 1991.

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(c University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Security of Provider sides in Data Privacy and Data Accessibility Issues in Cloud computing

Taimoor Ahamd, Hala Aslam, Shahzeb Shahzad Department of CS&IT University of Sargodha Gujranwala campus [email protected], [email protected] , [email protected]

Abstract: Cloud computing is an approach to share resources under one or more than one leading authority using multiple developments and deployment models such as resources of computational power and storage. Basically, the cloud is a business model, it has grown up in business and various fields of life as well. In spite of its power, it raises numerous security threats including loss of customer important data, data leakage, duplicating, resource pooling etc. As far as security threats are concerned, a wide research has been conducted which show threats with services and deployment models of a cloud. In order to realize these threats, this study is presented to effectively refine the basic security issues under various areas of cloud. This work presents data security threats under the cloud models. The solution is to involve third-party cloud provider in which client send their data to the cloud which is encrypted by third-party. The intention of proposed work is to save the cloud services providers from unauthorized access by blocking the unauthorized users.

Keywords: Cloud Computing, Grid Computing, Security threats, and Access control, Data Accessibility, Data Privacy, Authentication Security and Encryption.

addition, a number of threats arise for consumers when data I. INTRODUCTION is sent to the cloud. These threats are data threats, virtual Grid computing is a system in which different users are machine threat, user access threat, infrastructure threat and shared their resources to perform a specific task. Different physical security threat [6]. The proposed work will secure types of errors are created in grid computing such as the servers (third party and storage provider server) to access processing time, user interaction, resource sharing and from unauthorized users and blocked users. This study is limited area applications. Furthermore, grid computing has divided into different sections. Section 2 review the also some limitations like fault tolerance, error detection and difference between cloud computing and grid computing. scheduling. To minimize these errors and limitations the Section 3 explain the cloud models. Section 4 define the data researchers proposed an emerging technology called cloud threats in cloud computing. Section 5 give the solution. computing [1]. Section 6 has the conclusion. In cloud computing, the service providers offer different resources to their end users. These resources are II. DIFFERENCE BETWEEN CLOUD COMPUTING AND GRID computational power, storage resources, computer resources COMPUTING and software resources. According to “National Institute of Standards and Technology (NIST)”, cloud computing is Table 1: Difference between cloud and grid computing defined as “a model for enabling ubiquitous, convenient, on- demand network access to a shared pool of configurable Technologies Grid Cloud computing resources (e.g, networks, servers, storage, Architecture Sharing of users Resources applications, and services) that can be rapidly provisioned and released with minimal management effort or service resources provided by the provider interaction”[2]. Cloud model is made up of five providers important characteristics (on-demand self-service, broad Model Service Model Business Model network access, rapid elasticity, measured services and resource pooling), three service models (software as a service Access Limited access Complete access (SaaS), infrastructure as service (IaaS) and platform as a of resources of required service (PaaS)), and four deployment models (public, private, hybrid and community cloud) [3, 4]. resources

Security can be defined as “The state of being free from danger or threat” [5]. Security is a major element for consumers when they are shifting their data to the cloud. In University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:7-10

Development of Using Ready to use IV. DATA THREAT IN CLOUD COMPUTING applications executable file application In the cloud, customer faces many security problems and issues while sending their data . So, the data security is the components main factor that is decided by enterprise utilities used for Resources Not every time Available 24/7 cloud computing [9, 10]. Survey of Gartner in 2009 presented availability available that approximately 70% of the respondents in the actual deployment of cloud computing is security and privacy threats [11]. The client faces many threats in the cloud such

as data accessibility, data privacy, data confidentiality and data availability threat in the cloud environment. III. MODELS OF CLOUD COMPUTING

There are two models of cloud computing [4, 10]: A. Data accessibility threat: Data accessibility can be defined as “An access control system (ACS) is a type of security that manages and controls A. Deployment Models who or what is allowed entrance to a system, environment or This model includes private, public, community and hybrid facility. It identifies entities that have access to a controlled cloud or model in cloud computing. Normally, private cloud device or facility based on the validity of their is used by an organization and it is functioned by itself or credentials.”[12]. In simple words, data accessibility is to third party provider, whereas public cloud can be used by make a connection with the database where the customer more than one organization or general public. The store their data. The access to data is not provided to everyone community cloud can be used by a specific community who because of data security issues, data can be hacked or attacker has same purpose and requirements. The hybrid cloud is a can change and leak the private data of customers. To avoid combination of more than one cloud or model, for example, these issues, the access granted only to interrelated persons public and private, public and community, private and that are directly connected with a specific domain. The community etc [4, 10]. trustworthy persons who have to authenticate approval from the upper management to access the data and system for communication. The system requires some specific B. Delivery Models credentials from users who have authenticated with the rights The cloud provides different forms of services model like after this they will request and access the data. software, platform and infrastructure. Providers delivered Access to data plays an important role in the field of cloud costly applications like ERP and CRM to users. These computing because a person/company wants to save their applications run on provider platform that includes languages data where the person can access data anytime or anywhere. and libraries. The operating system, database, network Users prefer online access and services that are available bandwidth etc. Comes under infrastructure. There are 24/7. different types of cloud delivery models [8].

1) Software as a Service (SaaS) 1) Data Accessibility Issues: In this model cloud customers have control of utilities that According to the cloud policies, customer data are stored in are being delivered by the cloud providers. In SaaS customer different locations but clients are not familiar with the exact do not have control of infrastructure [8]. location of data.

Many scholars discussed data accessibility issues. Valuable 2) Platform as a Service (PaaS): data can be access by a user or third party. They can read, In this model cloud customers have control over platforms write, and modify the data [8, 14]. For example in a company, (tools and software) that is being provided by the cloud information is shared only with authorized users [10]. We providers. Cloud customers can use different types of cannot ignore the data security issues such as if an languages and tools to create their own applications [8]. unauthorized user attack on the private data then they can

modify data and leak the personal data of an organization 3) Infrastructure as a Service (IaaS): [11]. In the cloud, clients access data and services using the In this model cloud customers have complete control over internet that will create a risk for users. A user can implement cloud resources that are being provided by cloud providers access rights with its own policies [13]. such as storage, rent processing, network capacity and connectivity [8]. Mostly, researchers only discussed client level data accessibility. In cloud computing, the providers have rights

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:7-10 to access the client’s private data. Most of the cloud providers encrypt the data and have a chance to get the access of have stored customer data through encryption standards. customer data that harm the cloud users. Unauthorized providers know the encryption standards and technique that they are using for their customers. Through this, they can damage customer data [14].

B. Data Privacy Data privacy is known as information privacy. It can be defined as “data privacy, is the necessity to preserve and protect any personal information, collected by any organization, from being accessed by a third party. It is a part of Information Technology that helps an individual or an organization determines what data within a system can be shared with others and which should be restricted” [15]. In simple words, data privacy is justifying data that is saved in the cloud. Therefore, data privacy term is used specifically for the protection of data from illegal folks that occurs due to hacking. These days everything is possible when we are talking about the digital world where any obstacle is not taking more time.

Data privacy is essential when we are discussing the security of data. It is important for an organization to apply some security checks while accessing any data from cloud [15]. Figure 1: Referenced architecture

1) Data Privacy Issues In Figure 1 the user sends their data to the cloud. Firstly, it toward the third-party whose responsibility to encrypt the In cloud computing data privacy is the primary focus for a user data using encryption utility (which has more than one service provider. Cloud Suppliers tell that data is secured at encryption standards and customers have their own choice to their locations. Different researchers talk about data privacy choose the standard and encrypt their data using his desired issues. Their concepts are “Top vulnerabilities are to be key). After this, the encrypted data is sent to the cloud checked to ensure that data is protected from any provider's side and if a cloud provider admin accesses the attacks”[16]. Sensitive and non-sensitive data that comes data of a customer. The admin will only see the customer's from an authorized host can create threats with virtual co- data that is not understandable for them [18]. tenancy [13, 17]. Attackers get access to confidential data and moving towards data breaches. A researcher has revealed Now there is a danger to get unauthenticated or unauthorized malicious attacks through unauthorized operators by access to provider servers. The solution to this problem is targeting the IP address and physical server [4, 8]. when an irrelevant person wants to access the admin system.

It needs some credentials to log in. If credentials are From the prior discussion, the data privacy issues arise in authenticated then system send 6 digits code on admin's proposed work. When a user sends their data to the cloud, phone. Admin has to convey the generated code to the system they expect that their data can be secure and confidential. for performing their functionalities on the system. If the code Sometimes cloud data can be breached and altered by the is not authenticated then the server will be also shut down for attacker. The consumers faced disaster at the time of data that admin. retrievals due to these issues. It is needed to provide solutions that give complete privacy to users. If a hacker, provider and If the credentials are unauthenticated twice then the system admin access the data. The accessed data couldn’t be show non-running state for that user. At the same time, it understandable to them. There are also need to secure the blocks the IP address as well and put him on the block list. third party and storage server from unauthorized access.

VI. CONCLUSION V. SOLUTION Cloud computing is an emerging technology in nowadays In previous research, cloud providers use only one encryption that provides a great number of benefits, but it also contains standard at a time to encrypt the user data. The main issue in many security challenges. The main purpose of a cloud is to earlier research is that the cloud administrator knows how to store and manage the user data securely. When talking about

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:7-10 cloud system, it consists of development and deployment from Techopedia.” [Online]. models which a provider provides to their customers. In this Available:https://www.techopedia.com/definition/29707/access- paper, the proposed work discussed the security of third-party cloud and cloud storage providers. They count the number of control-system-acs. [Accessed: 17-Dec-2017]. logins attempt. If the login attempts exceeded to defined [13] R. Padhy, M. Patra, and S. Satapathy, “Cloud Computing: value than the system will go to un-running state for that user. Security Issues and Research Challenges,” … Inf. Technol. Secur. …, vol. 1, no. 2, pp. 136–146, 2011. [14] “Top Data Privacy Issues To Scare You In 2016 - InformationWeek.” [Online]. VII. REFERENCES Available:http://www.informationweek.com/strategic-cio/security- [1] “Grid vs CLoud Computing,” 2017. [Online]. Available: and-risk-strategy/top-data-privacy-issues-to-scare-you-in-2016/a/d- id/1323752. [Accessed: 11-Aug-2017]. http://www.brighthub.com/environment/green- [15] “Data Privacy - Definition & Types of Data.” computing/articles/68785.aspx. [Online]. Available: https://www.cleverism.com/lexicon/data- privacy/. [Accessed: 12-May-2017]. [2] F. Lui et al., “NIST Cloud Computing Reference [16] R. V. Rao and K. Selvamani, “ScienceDirect Data Architecture: Recommendations of the National Institute of Security Challenges and Its Solutions in Cloud Computing,” Procedia - Procedia Comput. Sci., vol. 48, pp. 204–209, 2015. Standards and Technology,” NIST Spec. Publ. 500-292, 2011. [17] B. S. Al-Attab and H. S. Fadewar, “Security Issues and [3] “What is SPI model (SaaS, PaaS, IaaS)? - Definition from Challenges in Cloud Computing,” Int. J. Emerg. Sci. Eng., vol. 2, WhatIs.com.” [Online]. Available: no. 7, pp. 22–26, 2014. http://searchcloudcomputing.techtarget.com/definition/SPI-model. [18] Taimoor Ahmad,Hala Aslam and Shahzeb Shahzad, [Accessed: 10-Aug-2017]. “Data privacy and Data Accessibilty issues in Cloud Enviornment” [4] B. Kaur, “Cloud Computing and Security Issues : A Survey Barinder Kaur,” vol. 3, no. 2, pp. 168–171, 2015. [5] “security - definition of security in English | Oxford

Dictionaries.” [Online]. Available: https://en.oxforddictionaries.com/definition/security. [Accessed: 10-Aug-2017]. [6] X. P. Xu, J. H. Yan, and L. Liu, “The Research on Cloud Computing Data Security Mechanism,” Adv. Mater. Res., vol. 846– 847, no. 3, pp. 1595–1599, 2013. [7] A. Hossain, B. Hossain, and S. Uddin, “Researched on Security Challenges with Possible Solution Strategies in Cloud Computing,” vol. 5, no. 2, pp. 31–39, 2016. [8] S. H. Rathi, “Efficient And Secure Privacy Preserving Data Storage and Auditability In Cloud Assisted Mobile Health Data,” no. February, 2015. [9] M. Ahmed and M. Ashraf Hossain, “Cloud Computing and Security Issues in the Cloud,” Int. J. Netw. Secur. Its Appl., vol. 6, no. 1, pp. 25–36, 2014. [10] R. Velumadhava Rao and K. Selvamani, “Data security challenges and its solutions in cloud computing,” Procedia Comput. Sci., vol. 48, no. C, pp. 204–209, 2015. [11] M. D. H. Parekh, “An Analysis of Security Challenges in Cloud Computing,” IJACSA) Int. J. Adv. Comput. Sci. Appl., vol. 4, no. 1, pp. 38–46, 2013. [12] “What is an Access Control System (ACS)? - Definition

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro

Website: http://sujo.usindh.edu.pk/index.php/USJICT/ Modification of Heun’s Iterative Method for the Population Growth Rate Problems

Aliya Pirzada, Asif Ali Shaikh,Syed Feroz Shah

Mehran University of Engineering and Technology Jamshoro, Pakistan [email protected], [email protected], [email protected]

Abstract: In this paper Modified Heun’s algorithm of Heun’s algorithm is presented with different formulations which are applied on exponential Population growth rate problems. In Heun’s algorithm the average of two formulations is considered as A.M mean, where as in Modified Heun’s algorithm G.M and Modified Heun’s algorithm H.M are also considered as averages which are also applied on exponential population growth rate problems respectively. Comparison between numerical results of both Modified Heun’s algorithm and existing Heun’s algorithm shows that Modified Heun’s algorithm of Heun’s algorithm is more convergent then Heun’s algorithm. Both algorithms will be analyzed by different errors for the convergent purpose.

Keywords: Exponential Population Growth Rate problems, Heun’s Algorithm, Modified Heun’s Algorithm, Convergence, Error.

I. INTRODUCTION method of order six which are used for ordinary differential equation. Differential equation arise from many problems in oscillations of mechanical and electrical system, bending of II beam, conduction of heat, velocity of chemical reaction etc, and such as play a very important role in all modern and In these three methods the implicit linear multistep method scientific and engineering studies. Differential equations of order six is more accurate and converges and faster than whether ordinary, partial or algebraic; that evolves change both methods [4]. Here is suggested Taylor’s series of some variables with respect to other variables. expansion algorithm of numerical solution for ordinary Mathematical models are very useful to solve real word differential equation, which are competes strongly with problems, most of differential equations are difficult to solve other existing algorithm [5]. Adams- Bashfourth method, analytically, then it must rely some numerical method to Runge Kutta method, Adams- Moultan method which are solve them, there are number of numerical method which are used for ordinary differential equation and stiff problems for used for differential equation to solve them numerically, like consistency, stability, and convergence [6]. There exist a Euler’s method Runge Kutta method Adms Bash fourth huge number of numerical methods that iteratively construct method etc. In this paper, we solve exponential population approximation to solution of ordinary differential equation growth rate problems using Heun’s algorithm and modified [7]. A numerical method namely, Stochastic terms involves Heun’s algorithm. There are many excellent and exhaustive a numeric are of pseudo random number for solving ordinary text on this subject that may be consulted such as, Euler’s differential equations. The present work is very useful for method is presented from the point of view of Taylor’s exponential population growth rate problems and shows algorithm and Runge Kutta method which are used on convergence in numerical results. ordinary differential equation for stability, accuracy, consistency, and convergence [1]. There methods are II. NUMERICAL METHOD present to solve initial value, problems, first order Euler’s, Numerical techniques forms an important part of solving second order Heun’s and rational Block method. The initial and boundary value problems in ordinary differential numerical results shows the block method is more equation; most important in case where there no closed form convergent then both methods [2]. A new nonlinear adaptive solution. Here is present some numerical algorithm Existing numeric solution for ordinary differential equation with Heun’s algorithm and modified Heun’s algorithm of Heun’s initial conditions the main features is to implement nonlinear algorithm. polynomial expansions in a nural network-like adaptive framework [3]. There are comparative studies of numerical Population Growth Rate Differential Equation methods for the numerical methods namely; Runge Kutta Problems. Differential equation dp/dt=kp of the Growth method, Euler’s method and an implicit linear multistep rate problems, p(t) be the quantity that increase with time

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t, in ordinary differential equation k>0, where k is … (5) pn1  pn  h gtn, pn gtn  h, pn  hgtn, pn  proportionality constant and t is time. Then exact kt solution will be pt poe Heun’s algorithm for the 2gtn , pn gtn  h, pn  hgtn , pn  … (6) pn1  pn  h solution of differential equation is described as gtn , pn  gtn  h, pn  hgtn , pn  a  b p  p  h … (1) where n1 n 2 Equation (5) shows the modified Heun’s algorithm by taking

a  gtn , pn , b  gtn  h, pn  hgtn , pn  Mean HM where as equation (6) represent modified Heun’s algorithm using H.M mean; now, relationship between Now re-write the Equation (1) by taking the functional averages in equations (2) (5) and (6) shows that A.M is values of a and b we got Equation (2). greater than G.M and G.M is greater than H.M i.e A.M > G.M > H.M. gtn , pn  gtn  h, pn  hgtn , pn  ... (2) pn1  pn  h 2 IV. NUMERICAL RESULTS

This is the Heun’s algorithm for the exponential population In this section, we have given some examples of exponential Growth Rate problems Population Growth Rate from open literature assess the performances of existing algorithm and modified algorithm III. PROPOSED/ MODIFIED ALGORITHM OF HUEN’S and compare the results of Heun’s algorithm with the ALGORITHM. modified Heun’s algorithm and both algorithms will be So, here a and b obtained from AM which is Heun’s analyzed by various errors. Here the main focus of this algorithm. research is on increasing convergence rate of modified Heun’s algorithm to Heun’s algorithm. Here consider different Means such as G.M and H.M for modification in Huen’s algorithm. Example 1: Consider Exponential Population Growth rate problem Using a and b in Equation (1) we got Equation (3) and (4) dp  kp, given that t 0.1,1 dt pn1  pn  h ab … (3) The exact solution is given by A 100e0.250679566129t 2ab p  p  h …(4) Again we will n1 n a  b substitute a and b in equation (3) and (4) respectively we get equations (5) and (6)

Table 1: Minimum Error in H.M

Time Exact A.M A.E G.M A.E H.M A.E 0.1 102.538 102.632 0.094 102.629 0.091 102.626 0.088 0.2 105.141 105.334 0.193 105.327 0.186 105.321 0.180 0.3 107.810 108.106 0.296 108.097 0.287 108.087 0.277 0.4 110.547 110.952 0.405 110.939 0.392 110.926 0.379 0.5 113.353 113.872 0.519 113.855 0.502 113.839 0.486 0.6 116.230 116.869 0.639 116.849 0.619 116.828 0.598 0.7 119.181 119.945 0.764 119.921 0.740 119.897 0.716 0.8 122.206 123.103 0.897 123.074 0.868 123.045 0.839 0.9 125.308 126.343 1.035 126.31 1.002 126.277 0.969 1 128.489 129.668 1.179 129.631 1.142 129.593 1.104

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130 1.4 Exact time1 AM time2 1.2 125 GM time3 HM AME 1 GME 120 HME

0.8 115 0.6

110 0.4

105 0.2

100 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Figure 1.Exact and numerical resultsfor Example 1 Figure 2. Errors of numerical results for Example 1

As in given Table 1. We have obtained that the minimum existing algorithm. We infer that both numerical methods error of the in modified Heun’s algorithm of HM mean. solve the problems quite well. Modified algorithm is convergent than existing algorithm of mean AM. So, both methods are convergent, but Example 2: Exponential Population Growth rate problem modified algorithm is more convergent than Heun’s dp  kp, given that t 0.3,3 algorithm. The error of the numerical solution gets smaller dt form Table 1. We find that numerical results are accurate The exact solution to this problem is given by as numerical solution is close to the exact solution. The N 150e0.01335t error of modified algorithm of HM mean is less than

Table 2: Minimum Error in H.M

Time Exact AM AE GM AE HM AE 0.3 150.601 150.632 0.031 150.630 0.029 150.620 0.019 0.6 151.206 151.266 0.06 151.265 0.059 151.263 0.057 0.9 151.813 151.903 0.09 151.901 0.088 151.899 0.086 1.2 152.422 152.543 0.121 152.54 0.118 152.537 0.115 1.5 153.034 153.185 0.151 153.181 0.147 153.178 0.144 1.8 153.648 153.83 0.182 153.826 0.178 153.821 0.173 2.1 154.264 154.478 0.214 154.473 0.209 154.468 0.204 2.4 154.883 155.128 0.245 155.123 0.24 155.117 0.234 2.7 155.505 155.782 0.277 155.775 0.27 155.768 0.263 3 156.129 156.438 0.309 156.43 0.301 156.423 0.294

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157 0.35 Exact time1 AM time2 156 0.3 GM time3 HM AME 155 0.25 GME HME

154 0.2

153 0.15

152 0.1

151 0.05

150 0 0 0.5 1 1.5 2 2.5 3 0 0.5 1 1.5 2 2.5 3

Figure 3. Exact and numerical results for Example 2 Figure 4. Errors of numerical results for Example 2

In the second example, the table 2 shows that the Example 3: Consider the Exponential population Growth minimum error in the HM. Modified Heun’s algorithm rate problem

Existing algorithm is less convergent than modified dp  kp, giventhat t 0.2,1.8 dt Heun’s algorithm is convergent, so modified algorithm is more convergent. The error of the numerical solution gets The exact solution to this problem is given by smaller form Table 2. The error of modified method is less p 10,000e0.1t than existing algorithm.

Table 3: Minimum Error in H.M

Time Exact A.M A.E G.M A.E H.M A.E

0.2 10202.0 10211.1 9.10 10210.9 8.9 10210.6 8.6

0.4 10408.1 10426.7 18.6 10426.2 18.1 10425.7 17.6

0.6 10618.3 10646.8 28.5 10646.0 27.7 10645.3 27.0

0.8 10832.8 10871.5 38.7 10870.5 37.7 10869.5 36.7 1.0 11051.7 11101.0 49.3 11099.7 48.0 11098.4 46.7

1.2 11274.9 11335.4 60.5 11333.8 58.9 11332.2 57.3

1.4 11502.7 11574.7 72.0 11572.8 70.1 11570.8 68.1

1.6 11735.1 11819.0 83.9 11816.8 81.7 11814.6 79.5 1.8 11972.1 12068.5 96.4 12066.0 93.9 12063.4 91.3

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4 x 10 1.22 Exact 100 time1 1.2 AM 90 time2 GM time3 1.18 HM 80 AME 1.16 GME 70 HME 1.14 60

1.12 50

1.1 40

1.08 30

1.06 20

1.04 10

1.02 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Figure 6. Error of numerical results for Example 3 Figure 5. Exact and numerical results for Example 3 problems in Ordinary Differential Equations." IOSR Journal of Mathmatics 1 (2012): 25-31. As given in Table 3. obtain the minimum error of the [3] Mungkasi, Sudi, and Agung Christian. "Runge-Kutta and rational modified Heun’s algorithm, than existing method so both block methods for solving initial value problems." Journal of Physics: methods are convergent, We find that numerical results Conference Series. Vol. 795. No. 1. IOP Publishing, 2017. are accurate as numerical solution is close to the exact [4] Haweel, Tarek I., and Tarek N. Abdelhameed. "Power series neural solution. The error of modified Heun’s algorithm is less network solution for ordinary differential equations with initial than existing algorithm. We find that both methods are conditions." Communications, Signal Processing, and their useful for Population growth rate problems. Applications (ICCSPA), 2015 International Conference on. IEEE, 2015. V. DISCUSSION OF RESULTS [5] Fadugba, S. E., S. N. Ogunyebi, and J. T. Okunlola. "On the Comparative Study of Some Numerical Methods for the Solution of We notice that in figures the result of modified algorithm Initial Value Problems in Ordinary Differential Equations." (2014). of Heun’s algorithm is more convergent than existing algorithm, and also error of modified algorithm is less [6] Mohazzabi, Pirooz, and Jennifer L. Becker. "Numerical Solution of than existing algorithm; so that modified algorithm is Differential Equations by Direct Taylor Expansion." Journal of Applied more convergent. Mathematics and Physics 5.03 (2017): 623. [7]Butcher, John C. "Numerical methods for ordinary differential VI. CONCLUSION equations in the 20th century." Journal of Computational and Applied Mathematics 125.1 We have attempted exponential Population Growth rate (2000): 1-29. problems using Heun’s algorithm as A.M which is combination of two functions value; modified Heun’s [8] Sergeyev, Yaroslav D. "Solving ordinary differential equations on the Infinity Computer by working with infinitesimals algorithm of Heun’s algorithm considered as G.M and numerically." Applied Mathematics and Computation 219.22 (2013): H.M; the modified Heun’s algorithm is faster convergent 10668-10681. in computation, because this algorithm is very useful for Population Growth rate problems. From numerical results [9] Riesinger, Christoph, et al. "Gpu optimization of pseudo random number generators for random ordinary differential we found that modified Heun’s algorithms is robust than equations." Procedia Computer Science 29 (2014): 172-183. existing algorithm. That is, the modified algorithm is more efficient to solve problems including those with [10] Lee, Hyun Geun, Jaemin Shin, and June-Yub Lee. "A Second- population growth rate. Order Operator Splitting Fourier Spectral Method for Models of Epitaxial Thin Film Growth." Journal of Scientific Computing 71.3 (2017): 1303-1318 VII. REFERENCES. [11] Paul, Susmita, et al. "Some Comparison of Solutions by Different [1]Barboza, Eudes Mendes, and Bruno Ribeiro. "Hénon type Numerical Techniques on Mathematical Biology equations with one-sided exponential growth." Topological Problem." International Journal of Differential Equations 2016 (2016). Methods in Nonlinear Analysis (2017). [12] Hatamzadeh-Varmazyar, Saeed, Zahra Masouri, and Esmail [2] Bosede, Ogunrinde R., Fadugba S. Emmanuel, and Okunlola J. Babolian. "Numerical method for solving arbitrary linear differential Temitayo. "On some numerical methods for solving initial value equations using a set of orthogonal basis functions and operational matrix." Applied Mathematical Modelling 40.1 (2016): 233-253.

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[13] Al-Smadi, Mohammed, Omar Abu Arqub, and Ahmad El-Ajou. ordinary differential equationns." Numerische Mathematik (2012): 1- "A numerical iterative method for solving systems of first-order 21 periodic boundary value problems." Journal of Applied Mathematics 2014 (2014). [16] T. Ken, Population Aging, Unfunded Social Security and Economic Growth. No.1552017. [14] D. Azevedo, C. Rodrigues, J. Peres, and M. von Stosch. "An efficient method [17] Zahed, B., et al. "Numerical Study of Operating Pressure Effect on for the numerical integration of measured variable dependent ordinary Carbon Nanotube Growth Rate and Length Uniformity." Transport differential Phenomena in Nano and Micro Scales 2.1 (2014): 78-85. equations." Engineering Applications of Artificial Intelligence 38 (2015): 24-33. [18] Cumsille, Patricio, Juan A. Asenjo, and Carlos Conca. "A novel model for biofilm growth and its resolution by using the hybrid [15] Augustin, F. and P. Rentrop. "Stochastic Galerkin techniques for immersed interface-level set method." Computers & Mathematics with random Applications 67.1 (2014): 34-51.

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(c University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Agricultural Environmental Monitoring: A WSN Perspective.

Mushtaque Ahmed Rahu1, Pardeep Kumar2, Sarang Karim3, Azeem Ayaz Mirani4

1Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah 2Department of Computer System Engineering, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah 3Institute of ICT,Mehran University of Engineering & Technology, Jamshoro 4PhD Scholar Department of CSE, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah [email protected], [email protected], [email protected], [email protected]

Abstract: In this article, we develop a system based on WSN platform to measure the data of crop parameters for the agriculturists aimed to enhance the quality and quality of the crops. The proposed system will minimize the exhaustive field visits of the farmers. This system will facilitate the precision agriculture by measuring three most key parameters (temperature, humidity and light) of the crops. The proposed system consists of IRIS motes MDA100CB data board, and MIB520 USB interface board. These are considered as sink and sensor nodes respectively. TinyOS is used to develop the code for WSN nodes, and GUI device is composed in Microsoft Visual Studio-2008 for displaying the measured data and stored in data base accordingly. ZigBee IEEE 802.15.4 and direct topology are utilized for the correspondence of sensor nodes with the base station.

Keywords: Wireless Sensor Network, Agricultural Environmental Monitoring,Data Acquisition, TinyOS

wireless sensor networks have been utilized in number of I. INTRODUCTION real time applications. WSN is a system which is comprised Modern agriculture system needs electronic devices and of sensors, radio frequency (RF) transceivers, technologies that can improve the production efficiency, microcontrollers, power sources and base station. Wireless production quality, post-harvest processes, and would be Sensor network can be possibly employed in different fields able to reduce environmental impacts. Automation in such as environmental monitoring, high ways, agriculture has brought about a fundamental role to what is buildings/structures watch out, military surveillance, and now known as smart agriculture [7] (also known as industrial and manufacturing automation in factories [1], precision agriculture) [1, 2]. Agriculture is the cultivation of [2], electronic commerce, indoor climate control, military animals as well as humans, although plants and other life surveillance, intelligent alarms, habitant monitoring, patient forms for food and to make them source, fiber, biofuel, apart monitoring, agriculture sector and irrigation [9], [10]. For from that medical and other products used to put up and to agriculture wireless sensor networks can help for monitoring enlarge human life [1]. The main source of livelihood of the fields and crops, thus sensor networks are helping mankind is agriculture. Agriculture plays vital and crucial farmers to prevent damages to their crops and increasing role in developing countries including Pakistan; it provides crop yield. Wireless sensor networks can be used specially large scale employment to the people. However, agriculture in agriculture sector to display key parameters of the certain is highly dependent on climate and weather. For instance, crops; hence in crops cultivation these can help farmers [3]. change in humidity, temperature and carbon dioxide may WSNs handling such a wide range of applications also share result in low yield of cash crops. The most important cash a set of characteristic requirements which majorly include crops are cotton, wheat, sugar-cane, etc. [8]. So, it is lifetime, real time, fault tolerance, scalability, significant to monitor environmental parameters in order to programmability, maintainability, security, production cost manage crop growth and improve the production, and latest & QoS [3]. technology is being implemented in agriculture to save time, In this paper Mushtaque Ahmed Rahu1 contributed in data use of fertilizers, use of pesticides, to condense human collection, manuscript writing, statistical analysis, and struggles, to decrease the expenses in cultivation of the interpretation of results. Dr. Pardeep Kumar2 contributed in crops. proposed topic, study design and methodology. Sarang Karim3 contributed in Literature review, referencing, quality Wireless sensor networks are quickly gaining popularity [8] insurer, discussions. Azeem Ayaz Meerani4 contribution is due to the fact that they are potentially low-cost solutions to technical support. a variety of real-world challenges [12] and troubles. The University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:17-24

II. SYSTEM OVERVIEW V. RELATED WORK Main System block diagram is given in Figure 1. The The huge number of research work has been conducted in proposed system contains different devices i.e. IRIS motes; the past relating to the field of wireless sensor network and a sink node and three sensor nodes, sensors; temperature- it’s characteristic. The sensor network has been utilized in Humidity and LDR and display device. The medium used is the number of real time applications. Wireless medium used for Sink and sensor nodes and for Shivaraj, B., and Natarj Urs U.D., (2015) [3] presented a communication direct topology has been used. With sensor summary review on few selected WSNs implementation board sensors are attached which are in contact with IRIS architectures which have been used for environmental motes. The data is sensed through sensors and same data is monitoring and summarized and contrast selected WSN transferred to base station by sensor nodes, where sink node implementations under different headings highlighting the receives the data with the help of MIB520CB programmable advantages and disadvantages. board, serially sink node is connected with computer via Korake, P.M. and Bhanarkar, M.K. (2015) [6] presented USB (Universal Serial Bus) port. End user can see the data an anticipated system, optimization of temperature and on a GUI tool without any difficulty. humidity measurement WSN node usingATmega328 for grapes environmental conditions checking, lot of systems are facilitated in the market which are based on Wireless sensor network (WSN) but this method is portable, small size and more energy capable. Sensor is integrated package contains temperature and humidity measurement capability in one package. Kumar, K.A, et al. (2014) [10] have toiled on fluoride affected area remote monitoring system using GIS, GPS, then GPRS systems. This design enables users to access the status of fluoride sensor at remote station on their cell phones via Internet. Government organizations and ordinary Figure 1: System block diagram people can also make use of this system to monitor affected areas of fluoride. III. MOTIVATION Devadas, R., et al (2010) [27] have made an instrumentation setup for monitoring the water and Nitrogen Meanwhile farmers are incapable to gaze the entire field and of wheat crops. They have developed WSN based system protecting it from risky (rain and floods etc.) environmental for collecting shadowy data crucial in 7 constricted-mobs conditions, soil moisture, watering, observing the level of (470, 550, 670, 700, 720, 750, 790 nm) on behalf of yield water of entire crops in malicious time. Farmers cannot progress surveillance on distinct spatial sampling achieve all the goals and also to have looking at the field to approaches. Spectral data measured in intervals up to 30 defend it from seed eating birds. So, for accomplishing these seconds were transmitted at field site base station via responsibilities easily, quickly and consequently Electronic wireless multi-hop network. Moreover, these data were Devices are required. We can sense possibility occurrence dispatched at remote station via broad-band internet access. of the unwanted situations, environmental unwanted Authors have compared the results obtained from sensor situations including Temperature, Humidity, Light, etc. by network with the industry based spectral radiometer and wireless sensor networks. Observing of these farm duties is found some differences in spectral measurements for 790nm not possible for menfolk. Therefore, usage of sensor while for all other bands no any difference were found. networks would condense farmers time and efforts.

VI. SYSTEM DESIGN IV. SCOPE OF WORK Enlisted below is the Scope of the work. A. System Architecture ▪ Recognition of unwanted environmental situations The whole system physical view is shown in Figure 2. ▪ Reduce agriculturalists energies and period There are three source motes; at base station direct topology ▪ Observing of crops: through database on Regular basis is used with a sink node. Sensor motes have been deployed ▪ Finally, budget/harvest decreased and profit/harvest in cultivating area away from each other on three different improved. locations. The arrangement of these sensor motes with three sensors, LDR and Temperature-Humidity sensor for computing the data of yields is given. Architecture system directs about the hardware which elaborates in monitoring of precision agriculture. A Personal Computer at base station is for showing results and for maintaining records in

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:17-24 database of measured data of field and post processing. The batteries are used as a power source for sensor nodes.

Figure 4: Complete portrait of sensor node

Figure 2: Architecture of system and physical view C. Sensor Node

We have used IRIS motes for the experimental work. In B. Deployment of Prototype System TinyOS it is duly programmable Environment. The At Qazi Ahmed city crop fields and the surrounding rural complete portrait of a sensor node is shown in Figure 4. areas the experimental work and real time deployment of Various devices are equipped in sensor node i.e. sensor nodes carried out. The distance between surrounding Microcontroller Board, MDA100CB Board and rural area is far from each other. As shown in Figures 3a & (Temperature, Humidity and LDR) Sensors. 3b. At the time of experimental work sink is placed at approximately 10 meter far from its adjacent source mote & VII. SYSTEM METHODOLOGY remaining two source motes existed little far from the sink node. About 60 minutes the experimental work carried out To reduce the huge efforts & energies of farmers the in morning, afternoon and evening. Approximately Four scenario and delivered some practices to increase the quality weeks to collect the results from the resultant motes. Apart production of yields have been deliberated. The applied from temperature-Humidity and LDR measurement were inputs by farmers such that fertilizers, pesticides, water, etc. also measured. at proper time and proper quantity and obviously, there will be increase of production slightly. In this system, a code has been developed. This is wireless sensor network Designed based, data sensed by sensors then that data communicated by a radio transmitter to the base station, wherefrom essential directions have been adopted. On a GUI display tool the data is exposed, and can be kept that data into database. In Figure 5 the research methodology flowchart. List of Devices Used in the Proposed System: In the planned system following devices have been used:

▪ Programmable USB Interface Board MIB520CB [31] (a) Sink node & three sensor nodes ▪ Data Acquisition Board/ Sensor Board MDA100CB [33] ▪ IRIS Mote XM2110CA, 2.4 GHz [32] ▪ Sensors (Temperature-Humidity, LDR Sensor) [34] ▪ Microcontroller Atmeag8 [30] ▪ Active and Passive Components (Diodes, RLC, etc.)

(b) Three sensor nodes

Figure 3: Deployment of prototype system

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B. Data log Window

The data transmitted by the source nodes is described in data log window as shown in above given Figure 7, that data is fed directly into database file in Microsoft Access format. We can observe the data of each node in this data log having parameters; temperature, humidity and LDR with time and date. The features of Data log are it shows the data of every node independently and merged. As mentioned in the Figure: 7 Data of the nodes of specific date and time can also access by pressing the Display Button.

Figure 5: Research methodology

VIII. DISPLAY TOOLS

A. GUI Tool

As in Figure 6, in Visual Studio 2008 main GUI tool is Figure 7: Datalog window for nodes data intended for detecting the received data from dissimilar three Sensor nodes. Every node consists of three IX. RESULTS AND DISCUSSIONS sensors; LDR & Temperature-Humidity sensor. Three nodes in Figure 6 are shown. For connecting the GUI A. Temperature Measurement tool with MIB520 programmable board connect button Temperature measurements of all three nodes at different is given and is accordingly connected to (Sink Node) transmission time and session (morning, afternoon and IRIS Mote through serial communication with PC. night) as in Figure 8. The graphs given below are showing From these three source nodes Sink IRIS mote receives temperature (in Celsius) w.r.t transmission time measured data. For displaying the source nodes sent data display by Node 1, Node 2 and Node 3. From following graphs, it is data log button is used. Each node data can be clearly acknowledged that the average measurements of all perceived instantaneously and independently in data log three nodes are in equalization. Well, the minor differences window. are come in cross due to variation in atmospheric pressure,

line of sight, calibration of devices, interference of signals, power consumption, and the distance between sink and sensor nodes.

Figure 6: Main GUI tool

(a) Measurement of sensor nodes (1,2 and 3) during morning

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indirect relation temperature, consequently humidity will be increased as temperature is increased. By comparing above temperature graphs in Figure 8 with humidity graphs in Figure 9, it justifies the above statement. During morning time humidity is low as the time passed and the temperature and sun light are increased accordingly, the humidity is also increased.

(b) Measurements of sensor nodes (1, 2, and 3) during noon

(a) Measurements of sensor nodes (1, 2, and 3) during morning

(c) Measurements of sensor nodes (1, 2, and 3) during night

Figure 8: Temperature measurements

B. Humidity Measurement (b) Measurements of sensor nodes (1, 2, and 3) during noon Humidity measurements of all three nodes at different transmission time and session (morning, afternoon and Figure. 9: Humidity measurements night) as in Figure 9. The graphs given below are showing humidity (in %age) with respect to transmission time measured by Node 1, Node 2 and Node 3. As humidity is graphs are for the detection of sun light. As we know that the sun plays a pivotal role and contains a significant consideration in agriculture sector, because the crops prepare the food from the sun light. Also, the length and growth of the crops are liable to sun light. The determined graphs are showing different states of the sun light. The intensity Lux is indirect relation with sun light, as sun light increases the value of Lux also increases and vice versa. While, during night, it can be observed that the values of Lux are almost in level at different states of transmission time. The variation has been occurred at late night due to

(c) Measurements of sensor nodes (1, 2, and 3) during night moon light.

C. LDR Measurement LDR measurements of all three nodes at different transmission time and session (morning, afternoon and night) as in Figure 10. The graphs given below are showing light intensity (in Lux) with respect to transmission time measured by Node 1, Node 2, and Node 3. Following

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LDR parameters. GUI tool helped in showing results comprehensible and clear. Every user can understand the results easily without concerning any research expert. Database is used to maintain up to date information about collected data by the sensor node. In last, inter-node distance either increase or decrease the data delivery rate of the sensor nodes. So, for required parameters measuring the (temperature, humidity and light intensity) of the field to its BS this project provides ease to the users, every time they are not required to visit at crops, however just deployment of motes ones & acquire the measured information at room,

(a) Measurements of sensor nodes (1, 2, and 3) during morning BS, at the office, etc. Thus, available platforms, major deployment areas and the architecture of a WSN have been conferred. Diverse techniques and methods have been declared, concerning data transfer towards precision agriculture. XI. RECOMMENDATIONS This work is about WSN system based on recommendations of crops according to suitable area as per environmental conditions (i.e. mud, water, temperature etc.) and instructions have been provided for crops to the growth according to environmental conditions. The data is continuously monitored at base station; detail of crops is given in above table Figure 11 and Map Figure 12.

(b) Measurements of sensor nodes (1, 2, and 3) during noon

(c) Measurements of sensor nodes (1, 2, and 3) during night

Figure 10: LDR measurements

X. CONCLUSIONS Figure 11: Recommendations about crop suitable area as per environmental conditions The System arrangement is based on practice wireless data acquisition system of field harvests through Wireless Sensor Networks. This offers the solutions to many difficulties challenged by farmers at crops. Performance of IRIS motes real time deployment has been shown. IRIS motes remained appropriately programmed to get the (temperature, humidity and light intensity) data of crops & to transmit that observed data to the BS through medium of wireless. So, this work is about WSNs deployment in different crop fields. So, the objective is attained and these results after the implementation of this system have been evaluated. Firstly, analyzing of temperature-Humidity and

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[5] M Monisha, TG Dhanalakshmi. (2015), A Review on Precision Agriculture and Its Farming Methods Research Journal of Pharmaceutical, Biological and Chemical Sciences, RJPBCS 6(3), ISSN: 0975-8585, Page No. 1142. [6] Korake, PM and Bhanarkar, MK. (2015), Humidity and Temperature Measurement WSN node for Grapes Environmental Condition Monitoring European Journal of Advances in Engineering and Technology, ISSN: 2394 - 658X, 2(5): page No/72-76. [7] Lachure, S, Bhagat, A and Lachure, J. (2015), Review on Precision Agriculture using Wireless Sensor Network International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.20. [8] Bansode, S, Raut, C, Meshram, P and V.R, P. (2015), An Automatic Identification of Agriculture Pest Insects and Pesticide Controlling International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE), ISSN 2349-7815 , Vol. 2, Issue 2, pp:21-28. [9] Xu, G, Shen, W and Wang, X. (2014), Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey ISSN 1424-8220. [10] Kumar, K.A, Lingam, P.G and Rao, K.M. (2014), Wireless Sensor Figure 12: Crops suitable area map as per environmental conditions Based Remote Monitoring System For Fluoride Affected Areas Using GPRS and GIS, International Journal of Computer and Technology (IJCTT) volume 7 number 4, ISSN: 2231-2803, pp: 178- XII. FUTURE WORK 182. [11] Chaitanya, N.K, Kumar, G.A and Kumari, P.A. (2013), Zigbee based This is the moment that states which is leading technology Wireless Sensing Platform for Monitoring Agriculture Environment in agriculture and is appropriate for certain & surrounded International Journal of Computer Applications (0975 8887), applications as. It is seen that the machinery, methods Volume 83 No.11. techniques and actuality used in deployment setups do not [12] Mishra, D.P and Kumar, R. (2013), Wireless sensor network design for paddy Crop monitoring of chhattisgarh International Journal of linked to only field or technology however, combinations, Advanced Engineering Research and Studies, E-ISSN22498974, different technologies and group of many fields altogether 107-111. remain linked. So, an abundant sympathy is constructing to [13] Satyanarayana, G.V and Mazaruddin, SD. (2013), Wireless Sensor use of wireless sensor networks, for its enhanced latent & Based Remote Monitoring System for Agriculture Using ZigBee and GPS, Conference On Advances in Communication and Control consequence. Numerous equipment has been used widely Systems (CAC2S 2013) pp: 110-114. for precision agriculture as last few decades. Hence system [14] Vijayalakshmi, A and Ranjan, P.V. (2013), Code Strategy Algorithm can be used in large areas, multiple nodes can be used and For Online Power Quality Monitoring Of Electrical Equipment multi hoping can be used. Simultaneously by applying one Using WSN Under Tiny-Os Environment, Journal of Theoretical and Applied Information Technology, Vol. 57 No.3, pp:465-473. or more than one following areas this work can be enriched. [15] Awasthi, A and Reddy, S.R.N. (2013), Monitoring for Precision Agriculture Using Wireless Sensor Network-A Review, Global XIII. ACKNOWLEDGEMENTS Journal of Computer Science and Technology Network, Web & Security Volume 13 Issue 7 Version 1.0. [16] YU, X, WU, P, Wang, N, Han, W and Zhang, Z. (2012), Survey on This work is carried out under the financial support of Wireless Sensor Networks Agricultural Environment Information Quaid-e-Awan University of Engineering, Science and Monitoring, Journal of Computational Information Systems 8: 19, Technology, Nawab shah, Pakistan and Mehran University pp: 79197926. [17] Murillo, F.A, Pena, M and M, D. (2012), Applications of WSN in of Engineering and Technology, Jamshoro, Pakistan. Health and Agriculture, IEEE Communications Conference (COLCOM) Columbian, pp: 1-6. XIV. REFERENCES [18] Mampentzidou, I., Karapistoli, E., and Economides, A.A., (2012), Basic guidelines for deploying wireless sensor networks in agriculture, in Ultra-Modern Telecommunications and Control [1] Singh, B and Harender, D. (2015), Intelligent Monitoring & Systems and Workshops (ICUMT), 2012 4th International Congress Controlling of Agricultural Field Parameters Using Zigbee, on. IEEE, 2012, pp. 864869. International Journal in IT and Engineering, ISSN: 2321-1776, [19] Liakos, T.A, Perlepes, V, Fountas, L and T, S.G. (2011), Wireless Vol.03 Issue-01. Sensor Network for Precision Agriculture, IEEE Panhellenic [2] Chaudhari, M.S, Jaiswal, R, Birade, C. and Bhapkar, V. (2015), Conference on Informatics, pp: 397-402. Wireless Sensor Network as a Tool for Supporting Agriculture in the [20] Jiber, Y, Hamid, H and Ahmed, K. (2011), Precision agriculture Precision Irrigation System International Journal of Advanced monitoring framework based on WSN. In IWCMC, pages 2015- Research in Computer and Communication Engineering Vol. 4, Issue 2020. IEEE 2. [21] Valente, J, Sanz, D, Baarientos, A, Cerro, J.D, Ribeiro, A and Rossi, [3] Shivaraj, B., and Natarj Urs U.D., (2015), Wireless Sensor Networks C. (2011), An Air-Ground Wireless Sensor Network for Crop for Environmental Monitoring: A Theoretical Review Journal of Monitoring, ISSN 1424-8220, pp: 6088-6108. electronics and computer science, ISSN- 3967-0867. [22] [22] Li, S., Cui, J. and Li, Z. (2011), Wireless Sensor Network for [4] Zhang, Z., Yu, X., Wu, P. and Han, W. (2015), Survey on Water- Precise Agriculture Monitoring, Fourth International Conference on saving Agricultural Internet of Things based on Wireless Sensor Intelligent Computation Technology and Automation, pp: 307-310. Network International Journal of Control and Automation Vol. 8, No. 4, pp. 229-240.

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[23] Atmga8 Microcontroller Datasheet, http://www.atmel.com/Images/Atmel-2486-8-bit-AVR- microcontroller-ATmega8 L datasheet.pdf [24] MIB520CB Datasheet,http://www.memsic.com/support/documents/wireless sensor-networks /category/7- datasheets.html?download=144%3Amib520cb [25] IRIS Mote Datasheet, http://www.dinesgroup.org/projects/images/pdf files /iris datasheet.pdf [26] MDA100CBDatasheet,http://www.memsic.com/support/documentati on/wireless-sensor- networks/category/7datasheets.html?download=141%Amda 100 [27] DHT11 Sensor Datasheet, Temperature and Humidity Module, http://www.micropik.com/PDF/dht11.pdf/ [28] Tiny OS, an open-source operating system for wireless embedded sensor networks, http://www.tinyos.net/ [29] Technical Guide. Agriculture 2.0 document version: v4.0 02/2013. Libelium communications Distribuidas S.L. http://www.libelium.com, 2013. [30] Jawad, H, M., Nordin, R., Gharghan, S, K., et al. (2017), “Energy- efficient wireless sensor networks for precision agriculture: A review” Sensors 2017, 17, 1781; doi:10.3390/s17081781. www.mdpi.com/journal/sensors. [31] Rubala,J,I.,Anitha, D., (2017), “Agriculture field monitoring using wireless sensor networks to improving crop production” International Journal of Engineering Science and Computing. Volume 7 Issue No.3. [32] Jaladi, A,R., Khithani, K., Pawar,P., et al. (2017), “Environmental Monitoring Using Wireless Sensor Networks(WSN) based on IOT” International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395 -0056, p-ISSN: 2395-0072, Volume: 04 Issue: 01. [33] Ali, A., Ming, Y., Chakraborty,S., Iram, S., (2017) “A Comprehensive Survey on Real-Time Applications of WSN” www.mdpi.com/journal/futureinternet.

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ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/ Software Outsourcing Cost Estimation Model (SOCEM). A Systematic Literature Review Protocol Jamshed Ahmad, Abdul Wahid Khan, Iqbal Qasim Department of Computer Science, University of Science & Technology Bannu, Pakistan [email protected], [email protected], [email protected] ______

Abstract: Outsourcing software enhancement aims at developing top quality product at a low cost. This type of business is expending slowly and steadily. We have to work on developing a model called SOCEM. Its purpose is to overcome the challenges being faced by different organizations regarding cost estimation. Our work will be related systematic literature review (SLR). It will help in identifying all those challenges being faced by outsourcing software vendors’ organizations. Different types of techniques and world practices will be collected in this regard. We will use a new approach of SLR in this study. The primary theme of this paper is to work on a model called software outsourcing cost estimation challenge model for the identification of all those challenges being faced by a software outsourcing vendor organization regarding cost estimation. This model will assist vendor organizations to estimate properly before contracting with client organization. Keywords: Offshore software development outsourcing (OSDO); Vendors; Systematic literature review; critical challenges, SOCEM.

product. In order to get survival, we have to focus on low I. INTRODUCTION cost[11]. There are four cost estimation methods i.e. We are living in the era of competition. Everyone in the Traditional Cost Estimating, Feature Based Costing, field of business wants to lead other. In today’s age the Parametric Estimating , Neural Network Based Cost fulfilling of customers’ needs is the primary goal of every Estimation [11]. It has become a global challenge to address businessman. Only outsourcing can achieve this purpose [1]. software cost estimation[12]. In order to run successfully, it Outsourcing is defined as findings something from the is necessary for software to accurately estimate its cost[13]. outer[2]. Outsourcing has another definition as an agreement As outsourcing claims so many advantages, but is also not between vendor and client. Vendors then return agreed free from weaknesses[14]. Collecting low priced products services for payment[3] .This is the right time for from outside does not guarantee in competitive outsourcing innovation. At early stages, outsourcing main advantages[15]. So many researcher has reached on the purpose was to focus on low cost, but nowadays it also decision that there are wide range of challenges in focuses on improving services and business activities as well. outsourcing results in unexpected results, unexpected raised It also focuses on internal and external demands [4]. Every costs, disputes, lock-in and organizational loses[14]. pattern does come in with different and exclusive designs in mind therefore make them the project different. When there is a change in market price or some other competitive value II. BACKGROUND in the market, an organization must also consider it by using Global software development has effected every industry their own core competencies[5]. Core competencies are the globally whether it is small or large[16]. GSD is like a big combination of knowledge, technologies, skills, information umbrella. Working on GSD, researchers and scientists from and methods that provide the product or services that the different time zones and culture have reached on the occasion customer wants to take[6]. Four strong powers driving the that lack of information is the critical challenge in software outsourcing innovation.[7]. These powers are demand, supply outsourcing[17]. While using GSD, engineers and of scientists and technologists, interaction capabilities and researchers belonging from different areas, countries and new innovations. There are so many theoretical justification time zones are struggling for the development of software. for outsourcing[8]. Most important one is Transaction cost Using GSD, the main problem is the communication and theory, agency theory etc. the impact of outsourcing is not mismanagement [18]. The idea of outsourcing was first only on international trade but it also deals in changing its presented by Ross Perot, at the time when he did find pattern[9]. It is the need of today to develop high quality Electronic Data Systems (EDS) in 1962[19]. Outsourcing software. Its basic purpose should be on fulfilling customers means to produce outside one`s company[19]. Software both implicit and explicit needs[10]. In today’s business outsourcing is gaining its popularity in a fast way, it has world cost is the most important factor in preparing the provided a new structure to the business activities[20]. University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:25-30

Outsourcing comes with two types i.e. offshore outsourcing 2. We don’t have well trained estimators. and inshore outsourcing[19]. Off shoring can be stated as 3. No more penalties associated with cost estimation etc. combining business activities to lower cost areas across country`s boarder line. In shoring believes in combining 4. In outsourcing cost estimation, it creates hurdles when we business activities from higher paid countries to lower paid do estimates at early stages. [28]. countries. Software cost estimation believes in process of identifying the effort, time and cost required to complete the There are seven steps in cost estimation[29]. project successfully[21]. There are two drivers for successful 1. Objective of cost estimation should be defined. outsourcing. These drivers are cost reduction and strategic 2. For required data and resources, there should be well shift. developed project plan. Software cost estimation is defined as it is the repetitive 3. Software requirements should be pinned down. process in order to develop an approximation of the money 4. Collect as much detail as required about the feasibility of related resources needed to success complete activities in the software system. project. Or we can also defined it as Software cost estimation 5. Cost estimation techniques should be used in several is the process for telling whether a product will be shapes. comfortable or not[22]. Going into the depth of the history of 6. Make comparison between estimating process. software cost estimation, it starts its beginning in 1960s, 7. Soon after starting, monitoring system must be developed when rule of thumb was dominating the field of business. for monitoring the progresses. After that Berry W. Boehm and C. Abets developed COCOMO model[23]. Soon after that Berry W. Boehm Cost estimation methods can be categorized in two types. reconstruct this Model to COCOMO II[24]. It is consisted of Algorithmic method and non-algorithmic method. three sub models which are named as Application Algorithmic methods are dependendent on simple Composition, Early Design and Post- architecture models. . mathematical calculations[30]. Non algorithmic method Khan, et al[25] have developed Outsourcing Contract works on complex mathematical computations. The Management Model (OCMM) to assist outsourcing vendor challenges in exact prediction of cost, effort and time of organizations in addressing the challenge of poor contract software projects are increasingly day to day. To solve these management. Azeem, Muhammad Ilyas[26] has developed challenges, there should be a call for well-defined software Intercultural Challenges Mitigation Model (ICCMM) to estimation process[21]. Cost estimation Outsourcing, has the assist outsourcing vendor organizations in addressing following advantages and disadvantages intercultural challenges in outsourcing relationships. But the above tables have some limitations. Limitation of COCOMO Advantages are: - Improved flexibility - better cash flow - model is that it only focuses on cost of a product. How much Lower investment risk - Lower potential labor costs. cost comes on developing a product? Similarly requirement Disadvantages are: - choosing of wrong supplier - Intellectual elicitation model (RECM) focuses only on requirement elicitation challenges. Another model called Outsourcing property leakage- Losing in control over process - Long lead contract management model (OCMM) 0nly focuses on times/capacity shortages. assisting vendor organization in management and execution Our model is unique as compared to other models. Our model of contract outsourcing. There is no such model to identify the challenges while estimating a cost of a product. focuses on the challenges while estimating a cost. What type Improvements are continuing on regular basis up to now. of challenges we are facing while estimating a cost of a Day by day competition in the business world is increasing. product. Our research that we are doing may have some In such position, the need for software outsourcing cost limitations. In performing SLR, we may have missed some estimation is feeling hard. There are the following attributes relevant papers. As research in the field of software while working on good software cost estimation[7]. outsourcing is performed on daily basis, and there are

possibilities of adding more and more papers. Due to non 1. It is supported by project team and project manager as access to digital library and limited resources, we are unable well. to access all the related papers. We have planned to enhance 2. It is warmly welcomed by all stakeholders. our model to improve its usability in OSDO organization. 3. It is based on well-known software cost models. Our model will provide different activities for OSDO But there are also coming some difficulties with software vendors. These activities are as fellow: cost estimation.[27]. i.e. 1. We don’t have much historical database of cost • To tell about the challenges of software outsourcing measurement. cost estimation in software industry.

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• To tell about the practices in order to handle these Literature]…, “POPULATION”, for successful [Software challenges of software outsourcing cost estimation. Outsourcing Cost Estimation]... “OUTCOMES OF • To tell about the challenges whether they are weak RELEVANCE”.

or strong? C. Search Strategies • To create different assessment reports. 1. Trial Search

Work done by conducting trial search by using search string III. SYSTEMATIC LITERATURE REVIEW PROCOTOL in Science Direct, IEEEXplore, Web of Science and ACM This paper is protocol based which will give road map for digital library. the identification of challenges faced by the vendor organizations regarding cost estimation of software2. Trial Search String development. A plan is in our mind taking help from SLR as ((“software outsourcing” OR “information systems a research methodology to find challenges and practices. outsourcing” OR “IT outsourcing”) AND (“cost estimation” Practices are used for handling challenges. We use SLR as a OR “price prediction” OR “price forecasting”) AND systematic way for indentifying, extracting and evaluating (challenge OR risk OR barriers OR threat) AND (practice the entire data[31]. It has three phases. These phases include OR solution)) planning, conducting and review reporting[32]. Planning phase will produce systematic literature protocol that will Retrieval of the paper by using this search string will guide identify the objectives of the review. us for the development and validation of some large search terms and wanted protocol. A. Research Questions RQ1. What and how many challenges there are faced by D. Characteristics of Search Terms vendor organizations in software outsourcing cost Search terms/strings constructed under the following search estimation? strategy. RQ2. What is the real world practices used to handle the cost estimation issues being faced by the vendor organization in 1. For the inheritance of major terms, focus on research software development outsourcing? questions, by pointing out population, intervention and Outcome;

B. Construction of Search Terms 2. For these major terms, search out the alternate spellings We will use the following options while working on and same meaning words. designing a search term specific to our research questions. 3. Also focus on Verifying major words in any related Population: Outsourcing Software vendors, suppliers and articles. clients. 4. Notice should be taken whether the database allows Use Intervention: Challenges, practices, features. of Boolean Operators for conjunction to use ‟OR‟ operator Outcomes of relevance: useful software outsourcing, quick for the Concatenation of alternative spellings and same responsive software outsourcing model. meaning words whereas ‟AND‟ for the concatenation of Experimental design: studying empirical field, SLR most used terms. (systematic literature review), studying Theoretical field, opinion of software experts, applying case studies. 5. Summarize form should be adopted in search strategy.

In order to indentify the above details, an example is Results for a) presented regarding research questions. RQ1: Outsourcing Software, cost estimation, challenges, RQ1. process of vendor selection. [What challenges/ problems] “INTERVENTION”, which are to be side lined by [Outsourcing software vendor]… RQ2: Software outsourcing, cost estimation, solution. “POPULATION” for the purpose of designing an [Effective Results for b) outsourcing software cost estimation]...” RELEVANCE OUTCOMES”. RQ1: RQ2. Software outsourcing: (“software outsourcing” OR [What are the practices faced by practical world] “information systems outsourcing” OR “information “INTERVENTION”, as noted in [Software Outsourcing

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Technology outsourcing” OR “IS outsourcing” OR “IT IV. RESOURCES TO BE SEARCHED outsourcing” OR “CBIS outsourcing” OR “computer-based information systems outsourcing” OR “software facility We have searched the following search engines. management”) AND (“cost estimation” OR “price estimation” OR “cost prediction” OR “price prediction” OR 1. IEEEXplore (http://ieeexplore.IEEE.org/explore ). “forecasting cost” OR “price forecasting”)) 2. Google Scholar (www.googlescholor.om ). Challenges: (challenges OR challenges OR difficulties OR 3. Springer Link (www.springerlink.com). Risks OR “risk analysis” OR “critical factors”) Vendors: (Vendors OR service-providers OR dealers OR traders 4. ACM (www.acm.org/). OR marketers OR sellers OR Developers). 5. Science Direct (www.sciencedirect.com ). RQ2: 6. Research Gate (www.researchgate.com ). Software Outsourcing: (“Software outsourcing” OR “Outsourcing software” OR “Outsourcing software V. SELECTION OF PUBLICATION Management”) AND (“cost estimation” OR “price estimation” OR “cost prediction” OR “price prediction” OR This part of the research tells about the criteria that included “forecasting cost” OR “price forecasting”)). inclusion, exclusion and selection of primary resources. This paper will be focusing on software outsourcing cost Practice:(Reasonable Solution OR "good practice" OR estimation. practice OR "Lessons learned" OR "Process improvement" OR "Process enhancement" OR "Process A. Inclusion Criteria innovation"). Purpose of this criterion is to focus on which portion of the research papers will be used for data extraction. Criteria Result for c) defined as under. IT Outsourcing, criteria for selection of vendors, challenge 1. Studies that tell supplier’s capability for software cost analysis, outsourcing cooperation, vendor testing, estimation. relationships software outsourcing, Software outsourcing, 2. Studies that describe the critical challenges of software software outsourcing suppliers, cost estimation technique, governance, real practice, possible solution. outsourcing Vendor. 3. Studies that focuses on motivations for software Result for d) outsourcing. RQ1: 4. Studies that define challenges in software outsourcing. ((“Outsourcing software” OR “software outsourcing” OR 5. Studies that tell about the effect in the outsourcing “information systems outsourcing” OR “information software cost estimation. Technology outsourcing” OR “IS outsourcing” OR “IT outsourcing” OR “CBIS outsourcing” OR “computer-Based 6. Papers written in English language included. information systems outsourcing”) AND (“cost estimation 7. Those papers be Included whose title may match with “OR “price estimation “OR “cost prediction” OR “price software outsourcing cost estimation. prediction” OR “forecasting cost” OR “price forecasting”)AND (challenge OR risk OR barriers OR 8. Those papers be Included which contain keywords that threat) AND(outsourcing estimation” OR “price may match with those defined in the search string. estimation”)). RQ2: B. Exclusion Criteria ((Software Outsourcing “OR “software outsourcing") AND (“cost estimation” OR “price estimation” OR “cost Purpose of this criterion is to focus on which portion of the prediction” OR “price prediction” OR “forecasting cost” OR research papers will not be used for data extraction. Criteria “price forecasting”) (Solution)). defined as under. 1. Studies which are not relevant to the research questions.

2. Literature not matches to the Client or Suppliers.

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3. Studies don’t identify challenges of the outsourcing 2. Data that address the research questions. software cost estimation suppliers` perspective. The research questions will be addressed by the following 4. Studies not related to offshore outsourcing. data extraction: RQ1. What are the challenges observed by vendor 5. Studies not satisfy the outsourcing software cost organizations in cost estimation in software development estimation. outsourcing? 6. Studies which based on the expert opinions. RQ2. What is the real world practices used to handle the cost 7. Exclude all duplicate papers. estimation issues faced by the vendor organization in outsourcing software development? VI. SELECTING PRIMARY SOURCES In the data extraction, the data to be captured is presented as follow: Reviewing of titles, keywords and abstracts of the papers TABLE II. Data to Be Extracted will be performed by initial selection of primary resources. 1. Review Date Exclude all those things which are irrelevant to the problems. 2. Title Through complete review of the article inclusion and 3. Authors 4. Database exclusion criteria will be checked. As there were not related 5. Reference papers in cost estimation outsourcing challenges, therefore a 6. Sample Population large difference between initial selection and primary 7. Publication Quality Description 8. Methodology selection came. 9. Company size 10. Country / State TABLE I. Summary of Search Results 11. Year 12. Challenges that have a positive result on software Resource to be Initial Primary final selection development outsourcing vendors in outsourcing cost searched selection Selection estimation. Google Scholar 513 200 117 Challenges that have a negative result on software IEEE Explore 14 10 04 outsourcing clients in screening/selection of software Springer 1934 20 03 development outsourcing vendors in outsourcing cost ACM 22 11 02 estimation. Science Direct 12 09 07 Research Gate 13 8 6 Total 2508 258 140 IX. DATA SYNTHESIS VII. PUBLICATION QUALITY ASSESMENT Performance of quality measurement is done at final In data synthesis stage, one summary table will be created selection of publication. It goes parallel with data extraction. having columns including S.NO., risk or challenges, barriers, Its performance is based on the following questions. frequency etc. highlighting the list of all the challenges in 1. Is it with no doubt how the seller screening performed? software outsourcing cost estimation with their frequencies and percentages. 2. Is it with no doubt how the challenges of the outsourcing software cost estimation were identified? X. DIVERGENCE 3. Is it with no doubt that opinion expert was not taken? If there come any change in the protocol, we will mention it The factors pointed out above will be done as ‘YES’ or ‘NO’ and will put it to the new Appendix. or ‘NA’.

XI. ACKNOWLEDGMENT VIII. DATA EXTRACTION STRATEGY We are thankful to University of Science & Technology A. Primary Study Data Bannu for its Contribution in our research activities. Gathering of data from the publication is performed through this study. The data given below will be extracted from the REFERENCES publications. [1] Raja & Kherun, OUTSOURCING WITHIN A SUPPLY 1. Detail about Publication i.e. Title, Authors, CHAIN MANAGEMENT FRAMEWORK. 2006. Journal/Conference title, etc.

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[2] Hongxun, J., et al. I Research on IT outsourcing based International Conference on Global Software on IT systems management. in ACM. nternational Engineering, 2009. Conference Proceeding Series,, 2006. Vol. 156. . [3] Ali-Babar, M., Verner, J., & Nguyen, P. , Establishing [17] Curtis, B., Krasner, H., Iscoe., A field study of the and Maintaining Trust and i.S.O.R.T.J.o.S.a.S.p. 1438– software design process for large systems. (1988). In 1449. ACM International Conference Proceeding Series, 1998. [4] Rasha abbas, p.d., outsourcing software applications 2: p. 12-28. development. issues, implication and impact. 1997. [18] Curtis, B., Krasner, H., Iscoe, N., A field study of the [5] McIvor, R., What is the right outsourcing strategy for software design process for large systems. your process?”,. European Management Journal,, 2008. Communications of the ACM 31(11), (1988). Vol. 26,: p. pp.24-34. [19] Sayed-Ahmed**, G.E.a.A., A Global Shift in the Present [6] Greaver, M., Strategic Outsourcing: A structured IT Industry. 2002. approach to outsourcing decisions. Int journal, 2015. 2: [20] Abdul Wahid Khan, a.S.U.K., Outsourcing Contract p. 11-19. Management Model (OCMM). Conference, 2012. 2: p. [7] W. Royce, Software project management: a unified 1-9. framework, Addison Wesley, . 1998. [21] Ongere, A.O., Software cost-estimation review 2013: p. [8] Ang & Straub, outsourcing. 1998: p. 19. 53. [9] Hanson, F.a., impact of outsourcing. 2004. [22] Lederer, A.L., Prasad., Informations Systems Software [10] Singh, P. and U.R. Scholar, Offshore Agile Cost Estimating. International Conference 2009. 2: p. methodologies in Software Engineering: A Study. 2009. 22-23. [23] Royce, W., Software project management: a unified [11] Roy, D.R., Decision Engineering Report Series Edited framework, Addison Wesley, . 1998. by Rajkumar Roy and Clive Kerr MK43 OAL 2003. [24] D., M., Software estimating models: . . April, 2013. [25] Khan, A.W., Outsourcing Contract Management Model [12] P., K., Cost estimation for global software development (OCMM). journal, 2016. [online]. CiteseerX; 2006. , March, 2013. . [26] Azeem, M.I., Challenges Mitigation Model (ICCMM. journal, 2016. [13] Jyoti G. Borade, V.R.K., Software Project Effort and [27] Wesley., A., Software project management: a unified Cost Estimation Techniques. International Journal of framework. 2004. Advanced Research in Computer Science and Software [28] Y. F. Li, M.X., T. N. Goh., A Study of Genetic Algorithm Engineering, 2013. Volume 3(Issue 8,). for Project Selection for Analogy Based Software Cost [14] Aubert, B., Dussault, S., Patry, M., & Rivard, S. (1999). Estimation. IEEE International Conference on, 2007. 2: . Managing the risk of it sourcing. Paper presented at the p. 2-9. Proceedings of the 32nd Hawaii International Conference of System Sciences (HICSS), January 5–8, [29] B. W. Boehm, Software engineering economics. 19881. Hawaii, 1999. [30] W. S. Donelson, “Project planning and control”, Datamation,. 2000: p. pp. 73-80. [15] Grover, V., Cheon, M. J., & Teng, J. T. C. , The effect of [31] Kitchenham, B.a.C.C., Guidelines for performing service quality and partnership on the outsourcing of Systematic Literature Reviews in Software Engineering,. information systems functions. . Journal of Management 2007. Information Systems: JMIS, 12(4 (Spring)), , (1996). : p. [32] Kitchenham, B.a.C.C., Guidelines for performing 89–116. Systematic Literature Reviews in Software Engineering,. 2007. [16] Lopez, A., J. Nicolas, and A. Toval. . , Risks and Safeguards for the Requirements Engineering Process in Global Software Development. In Fourth IEEE

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/ Modified Linear Convergence Mean Methods for Solving Non-Linear Equations Umair Khalid Qureshi1, Zubair Ahmed kalhoro2, Ghulam Yaseen Bhutto1, R B Khokar1, Zohaib Ali Qureshi2

1Department of Basic Science & Related Studies, Mehran University of Engineering and technology, Pakistan 2Institute of Mathematics and Computer Science, University of Sindh Jamshoro, Pakistan Email: [email protected], [email protected], [email protected]

Abstract In this paper, we have suggested a Modified Mean Methods for solving non-linear equations. Proposed Methods have linear order of convergence. The proposed Modified Mean Methods are used to solve all possible roots of non-linear functions in simpler and easier way. The proposed Methods are working tremendous as compare to Bisection method based on iterations and accuracy. To examine the fallouts of few problems, which are related by the non-linear functions to observe the efficiency of develop Modified Mean Methods. C++ and EXCEL have been used for obtaining results and graphical representations to justify the proposed methods. Throughout the study, it has been observed that the developed Modified Mean Algorithms are better techniques for estimating a root of non-linear equations.

Keyword: Non-linear equations, bisection method, convergence analysis, accuracy.

I. INTRODUCTION Combining these methods for better accuracy and less During many years’ various scientist and researchers number of iteration is observed in number of have taken interest and given multiple methods for articles.(Masood Allame &Nafiseh Azad, 2012) and solving non-linear equations. The root-finding problem is (Mcdougall & Wotherspoon, 2013) are using Newton one of the most relevant computational problems. It arises Raphson Method and Bisection method to construct a new in a wide variety of practical applications in Physics, iterated method, which is more quickly convergence than Chemistry, Biosciences, Engineering, etc. As a matter of Newton raphson method, hybrid or new hybrid iterated fact, the determination of any unknown appearing method and midpoint Newton raphson method. implicitly in scientific or engineering formulas, gives rise Furthermore, Bisection Method somewhat called to root finding problem (Datta, 2012) and (Iwetan,2012). Arithmetic Mean between a & b. Recently researchers Due to their importance; several methods have been took interest and given good method, for instance suggested and analyzed under certain conditions. One of (Aruchunanet al, 2015) has presented a Modified the easiest root-locating methods is Bisection method. It Arithmetic Mean technique for ordinary Arithmetic Mean works when F(x) is a continuous function and it needs technique for the solution of fourth order Fredholm preceding information of two initial guesses, a and b, that Integro-differential equations, and the execution of is f(a) and f(b) have opposite signs, then estimate the Arithmetic Mean technique for estimating dense linear average of [푎, 푏], and then choose whether the root lies system relatedby numerical solution by (Muthuvalu and on [푎, a  b ] or [ a  b , 푏] . Repeat as long as the Sulaiman, 2012).In the light of above given research, this 2 2 paper has been proposed Modified Mean Methods for interval is sufficiently small. (Dalquist and Bjorck, 2008) solving non-linear equations. It has been realized that the and (Mathews and Fink, 2004) have recommended that a proposed Methods are performing better as compared to root `x` exists in (푎, 푏) with f(x) = 0. Even though the Bisection method. Bisection method is consistent, but it converges very slowly. Researchers have tried and are trying to increase II. PROPOSED METHODS the convergence rate of Bisection method through In this section, we have been developed several different techniques by (Chitra et al, 2014) and (Tanakan, numerical iterated methods by using difference Mean 2013). Moreover, one of the fast converging numerical Formulae, such as methods is Newton Raphson Method. It is more effective than the Bisection method, but sometimes Newton Bisection Method or Arithmetic Mean: Raphson method fails to locate the roots. However, once it converges, it converges quicker than Bisection method. University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:31-35

Bisection Method somewhat called Arithmetic By using (1) in Geometric Mean, we get Mean between a, b. Bisection Method or Arithmetic Mean approximation solution `m` by the iterative scheme. 푚 = √푎(푎 + 푓(푎)) a  b m  2 It is Modified Geometric Mean. Similarly, Geometric Mean Modified Harmonic Mean: m  ab F or a, b  0 By using (1) in Harmonic Mean, we get Harmonic Mean 2ab m  For a, b  0 2푎(푎 + 푓(푎)) a  b 푚 = 2푎 + 푓(푎) Quadratic Mean a 2  b2 m  It is Modified Harmonic Mean. 2 Cubic mean Modified Quadratic Mean: a3  b3 m  3 2 By using (1) in Quadratic Mean, we get Heronian Mean a  ab  b a 2  (a  f (a))2 m  m  3 2 Now, we modified these Means by using numerical technique, such as It is Modified Quadratic Mean. 푏 = 푎 + ℎ Modified Cubic mean: Where `h` can be written as ℎ = ∇푎 = 푓(푎), we get 푏 = 푎 + 푓(푎)(1) By using (1) in Cubic mean, we get Now,(1) Substitute in all the above Means, we get Modified Mean Methods, thus a 3  (a  f (a))3 m  3 2 Modified Bisection Method: a  b It is Modified Cubic Mean. m  2 Let (a,b) be an interval, for better approximation we Modified Heronian Mean: a  b convert that interval into sub-interval i.e. (a, ) or 2 By using (1) in Heronian Mean, we get a  b , we get ( ,b) 2a  a(a  f (a))  f (a) 2 m  3푎 + 푏 3 푚 = 4 or It is Modified Heronian Mean. 푎 + 3푏 푚 = Hence, these are Modified Mean Methods for solving 4 By using (1) in above, we get nonlinear problems.

푚 = 푎 + 0.3푓(푎) (I) III. CONVERGENCE ANALYSIS 푚 = 푎 + 0.8푓(푎) (II) In this section, we have given the main results of this paper. We will give Mathematical proof that the Open If root is closer than interval point `a` then we use (I) or If Mean Methods has linear order of convergence. root is closer than to interval point `b` then we use (II).It Proof is Modified Bisection Method or Modified Arithmetic Mean, Likewise By using Taylor series, firstly we are expanding 푓(푎)and [푎 + 푓(푎)] of order of term about `훼`, Modified Geometric Mean: we obtain

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:31-35

2 F(a) = 푒푛푓`(훼) + 표(푒 푛) − − − (𝑖) 2 2 2 e 푛 + (푒푛 + 푒푛푓`(훼) + 표(푒 푛)) & 푒푛+1 = √ 2 2 [푎 + 푓(푎)] = 푒푛 + 푒푛푓`(훼) + 표(푒 푛) − − − (𝑖𝑖)

Modified Bisection Method or Arithmetic Mean: 1 + (1 + 푓`(훼) + 표(푒2 ))2 푒 = 푒 √ 푛 푛+1 푛 2 By using (i) in Modified Bisection Method, we gain

2 Modified Cubic mean: 푒푛+1 = 푒푛 + 0.3(푒푛푓`(훼) + 표(푒 푛))

2 By using (ii) in Cubic mean, we get 푒푛+1 = 푒푛[1 + 0.3푓`(훼) + 표(푒 푛)]

OR 3 e3 + (푒 + 푒 푓`(훼) + 표(푒2 ))3 푒 = √ 푛 푛 푛 푛 푛+1 2 2 푒푛+1 = 푒푛 + 0.8(푒푛푓`(훼) + 푓`(훼) + 표(푒 푛))

2 2 3 푒푛+1 = 푒푛[1 + 0.8푓`(훼) + 표(푒 푛] 3 1 + (1 + 푓`(훼) + 표(푒 )) 푒 = 푒 (√ 푛 ) 푛+1 푛 2 It has shown that the Modified Bisection Method or Modified Arithmetic Mean is linear order of convergence, likewise Modified Heronian Mean:

Modified Geometric Mean: By using (i) and (ii) in Heronian Mean, we get

By using (ii) in Geometric Mean, we get 푒푛+1 2푒 + 푒 √(1 + 푓`(훼) + 표(푒2 )) + 푒 (푓`(훼) + 표(푒2 )) = 푛 푛 푛 푛 푛 2 푒푛+1 = √푒푛(푒푛 + 푒푛푓`(훼) + 표(푒 푛) 2

2 2 푒푛+1 푒푛+1 = √e 푛(1 + 푓`(훼) + 표(푒 푛) 2 2 [2 + √(1 + 푓`(훼) + 표(푒 푛)) + (푓`(훼) + 표(푒 푛))] = 푒푛 2 푒푛+1 = 푒푛√(1 + 푓`(훼) + 표(푒 푛) 2

Modified Harmonic Mean: Henceforth this has been shown that the Proposed Modified Mean Methods have linear order of By using (i) and (ii) in Harmonic Mean, we get convergence.

2e2 (1 + 푓`(훼) + 표(푒2 ) 푒 = 푛 푛 푛+1 푒 [2 + 푓`(훼) + 표(푒2 ] 푛 푛 IV. RESULT AND DISSCUSSION 2 The developed methods have been used on few 2(1 + 푓`(훼) + 표(푒 푛) 푒 = 푒 examples of non-linear functions and compared 푛+1 푛 2 + 푓`(훼) + 표(푒2 ) 푛 developed methods with the Bisection method in Table-1. From the numerical result of table-1, it has been observed Modified Quadratic Mean: that the Modified Mean Techniques are not only reducing iterations but also increasing accuracy perception. By using (ii) in Quadratic Mean, we get Mathematical package such as C++ and EXCEL have been used to justify the results and graphical representations of Modified Mean Methods, such as in Table-1

Table-1 FUNCTION METHODS NO OF ITERATION Root A E Bisection Method 23 1.9209e-7 Modified Bisection Method 7 1.9209e-7

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Modified Geometric Method 12 2.3842e-7 sinx-x+1 Modified Harmonic Method 12 1.93456 1.9209e-7 (1,2) Modified Quadratic Method 13 1.9209e-7 Modified Cubic Method 12 1.9209e-7 Modified Heronian Method 12 1.9209e-7 Bisection Method 25 5.96046e-8 Modified Bisection Method 9 5.96046e-8 Cosx-x3 Modified Geometric Method 22 0.865474 5.96046e-8 Modified Harmonic Method 18 1.78814e-7

(0.5,2) Modified Quadratic Method 20 5.96046e-8 Modified Cubic Method 23 5.96046e-8 Modified Heronian Method 23 5.96046e-8 Bisection Method 23 5.96046e-8 Modified Bisection Method 8 5.96046e-8 ex-3x2 Modified Geometric Method 21 0.910008 1.19209e-7 Modified Harmonic Method 21 1.19209e-7

(0.5,1) Modified Quadratic Method 21 1.19209e-7 Modified Cubic Method 20 1.19209e-7 Modified Heronian Method 20 5.96046e-8 Bisection Method 21 4.76837e-7 Modified Bisection Method 9 4.76837e-7 -x Modified Geometric Method 19 4.76837e-7 e -cosx 4.72129 -7 Modified Harmonic Method 18 4.76837e (4,5) Modified Quadratic Method 19 4.76837e-7 Modified Cubic Method 17 4.76837e-7

Modified Heronian Method 18 4.76837e-7 Bisection Method 23 1.9209e-7 Modified Bisection Method 8 1.9209e-7 4sinx-ex Modified Geometric Method 25 1.36496 1.9209e-7 Modified Harmonic Method 23 1.9209e-7

(1,2) Modified Quadratic Method 22 2.38419e7 Modified Cubic Method 24 1.9209e-7 Modified Heronian Method 19 1.9209e-7

2.5 1.5 2 1 1.5

1 0.5 0.5 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 3 5 7 9 11 13 15 17 19

Fig.1. Comparison of accuracy analysis for the case of problem 1 Fig.1. Comparison of accuracy analysis for the case of problem 2

Solving a Nonlinear Algebraic Equations by Mid-Point, V. CONCLUSION World Applied Sciences Journal 17 (12): 1546-1548, In this paper, modified mean algorithms have been 2012 ISSN 1818-4952 IDOSI Publications, 2012. designed to estimate the root of nonlinear equations. [2] Biswa, N. D., 2012, Lecture Notes on Numerical Through the research, proposed methods are performing Solution of root Finding Problems. better than Bisection Method in terms of accuracy as well as iteration point of view. In Corollary, it has been observed [3] Chitra S. et al, 2014, “Role of Bisection Method”, from the results and comparisons that the Modified Mean International Journal of Computer Applications Methods are providing far better results as compared to the Technology and Research, vol, 3, 533-535. Bisection method. VI. Reference [4] Aruchunan,E. et al, 2015, A New Variant of Arithmetic Mean Iterative Method for Fourth [1] Allame, M and N Azad,On Modified Newton Method for Order Integro-differential Equations Solution,Dept.

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of Mathematics and Statistics Curtin University Systems Arise from First Kind Linear Fredholm Perth WA6845, Third International Conference on Integral Equations, University Malaysia Sabah, Artificial Intelligence, Modelling and Simulation. School of Science and Technology, Malaysia, The Publishing House Proceedings of the Romanian Academy, Series A, of the Romanian Academy [5] DalquistG. and A. Bjorck,Numerical Methods in Volume 1 3, Number 3/2012, Pp. 198–20. Scientific Computing, SIAM. 1(2008). [6] Iwetan, C. N. et al, 2012, Comparative Study of the [9] Trevor J. M. & S. J. Wotherspoon, A simple Bisection and Newton Methods in solving for Zero Modified Newton Method to Achieve convergence andExtremes of a Single-Variable Function. J. of of order 1+ √2 , applied Mathematics Letters, NAMP Vol.21 173-176. published by Elsevier 2013.

[7] Mathews, J. H. and K. K. Fink, Numerical [10] Tanakan, S., “A New Algorithm of Modified Methods using Matlab, 4th Edition, (2004). Bisection Method”, Applied Mathematical Sci, Vol.7 (2013). [8] Muthuvalu, M. S. And J. Sulaiman, The Arithmetic Mean Iterative Methods for Solving Dense Linear

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(c University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Correlation of Online Risks and Harm among Teenagers in Bangladesh

Taslim Taher, Mohd Adam Suhaimi

Department of Information Systems, International Islamic University Malaysia (IIUM) [email protected], [email protected]

Abstract: This study explored the influence of four important factors: demographic, psychological, social and religiosity level upon two important aspects: online risks and harm among teenagers in Bangladesh. A total of 443 teenagers (203 boys and 240 girls) from 8 educational institutions in urban and rural areas participated in the survey that employed a 45-item questionnaire measuring the constructs on a 5-point Likert scale. The data were analyzed quantitatively using descriptive statistics and Pearson’s correlation tests. The results showed that age and Socio- Economic Status (SES) under demographic factors; low self-efficacy, risky behavior and practices under psychological factors; parents, teachers and peers under social factors and religiosity level are significantly correlated with online risks among teenagers in Bangladesh. In addition to that, age and Socio-Economic Status (SES) under demographic factors; low self-efficacy, emotional problems as well as risky behavior and practices under psychological factors; teachers and peers under social factors as well as religiosity level were also found significantly correlated with harm among teenagers in Bangladesh. However, gender under demographic factors was not found having any statistical significant difference regarding online risks and harm among teenagers in Bangladesh. The findings have important implications on what the authorities need to put in place to make the online environment safe for their children.

Keywords: Demographic Factors; Psychological Factors; Social Factors; Religiosity Level; Online Risks; Harm;

more vulnerable to online risks in many studies [9-14]. Girls I. INTRODUCTION were found more victim of cyberbullying compared to boys The impact of internet first became noticeable in the in the study of Hasebrink, Görzig, Haddon, Kalmus and 1980s. The whole world is being affected since its presence. Livingstone (2011) [15]. They reported of experiencing more The process of learning and education has been facilitated harm from online risks in a series of studies as well [9-14]. through it in many ways. There is no doubt about it. But, one In the study of Haddon and Livingstone (2012), children cannot deny its negative impacts as well. The people who are with higher SES were found experiencing more bullying mostly affected by the positive and negative impacts of compared to ones with lower SES in France [16]. internet are the teenagers. Since, they are the most passionate Psychological factors that were examined in this research and primitive users of internet [1]. Using internet for the sake are emotional problems, self-efficacy and risk-taking. of education, interacting with peers, entertainment, online Children suffering from psychological problems reported games as well as shopping are the most productive uses of about experiencing more harm from online risks in the study internet by teenagers found in the studies. On the contrary, of d’Haenens, Vandoninck and Donoso (2013) [17]. Their the negative conducts mentioned in the literature are findings also revealed that children with higher self-efficacy disturbing and abusing others, addiction to pornography as were less vulnerable to online risks. They were also found well as other bad habits [2-5]. taking proactive coping strategies more compared to those Child abuse by internet predators is becoming very having lower self-efficacy [17]. Children in Denmark and serious in developing countries, especially in Bangladesh. As Italy reported that their emotional problems compelled them a result of that, preventive measures must be introduced engaging in excessive usage of internet. Whereas in Portugal, before the situation becomes out of control in this region [6, Belgium and Bulgaria, risky behavior and practices were 7]. reported as the most important predictors for excessive internet use in the study of Lobe, Livingstone, Ólafsson and II. LITERATURE REVIEW Vodeb (2011) [18]. Risky behavior and practices were also Several factors were investigated regarding this aspect in investigated by several researchers in their studies [9, 19]. a series of studies. At a glance, demographic factors can be Social factors that were examined in this research are mentioned among them firstly. Age and gender contributed a parents, teachers and peers. The relationship of social factors significant difference in the study of Livingstone, Kirwil, with online risks and harm were investigated thoroughly in Ponte and Staksrud (2014) [8]. Older teenagers were found 33 countries of Europe such as Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia,

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Finland, France, Germany, Greece, Hungary, Iceland, Italy, houses alone only with their own devices. Due to lack of Ireland, Latvia, Lithuania, Luxembourg, Malta, Netherlands, enough spaces and infrastructure, these urban children Norway, Poland, Portugal, Russia, Romania, Slovakia, usually enjoy their leisure time by watching television and/or Slovenia, Spain, Sweden, Switzerland and United Kingdom browsing internet alone [6]. [16, 18, 20, 21]. In most of the cases, parental guidance and Since, their addiction to internet is increasing day by day, monitoring, teachers’ as well as peers’ role were found they have started experiencing psychological problems as significantly related with online risks and harm among the well as neglecting their duties. Due to excessive browsing teenagers. The more support they got from parents, teachers internet, they cannot manage time for their studies, finishing and peers, the more efficiently they dealt with these risks and homework, doing household activities etc. Even they fail to successfully cope with the harm generated from the risks. manage taking adequate food timely as well as having Influence of religiosity level upon online risks and harm enough sleep properly. Consequently, their attitude is among teenagers were successfully investigated in a series of changing rapidly and miserably. Many have become isolated studies conducted in Hong Kong and United States [22-24]. from their families, friends and societies cause of this [6]. The findings revealed that teenagers having higher level of The author reported several risky behavior and practices religiosity level were found less vulnerable to online risks conducted by the teenagers in his studies. Feeling no and eventually less victimized by harm. From the review of hesitation about developing friendship online with the past literature, it is expected that these factors may have strangers, online dating, interacting with new people are significant influence upon online risks and harm among mentionable among them. The researcher also mentioned teenagers in Bangladesh. that 49% of the respondents did not even care about people having false identities. In addition to that, 50% of the respondents reported of creating fake accounts to bother Demographic Factors other people [6]. ▪ Age Finally, this issue has drawn so much attention to media ▪ Gender that it has gained media coverage by the two prominent daily ▪ SES newspapers in Bangladesh – The Daily Star and The Daily Observer. According to their reports, around half of the teenagers in Bangladesh are facing cybercrime and they Psychological Factors ultimately felt helpless while facing them [28, 29]. ▪ Emotional Hence, this study was designed with the aim of achieving Problems two main objectives: (i) to explore the factors that are related to online risks among teenagers in Bangladesh and, (ii) to ▪ Self-Efficacy Online Harm explore the factors that are related to harm experienced from ▪ Risk-Taking Risks online risks among teenagers in Bangladesh.

III. METHODOLOGY Social Factors This section discusses the methodology the researcher ▪ Parents employed to get the relevant results. Therefore, data ▪ Teachers collection and instrument, population and sample as well as ▪ Peers analysis of data are presented chronologically in this section. A. Data collection and instrument Religiosity Level The researcher conducted a survey in eight educational institutions from urban and rural areas in Bangladesh to explore the factors that are related to online risks and harm Figure 1. Research Model. among the teenagers. Survey was administered personally by entering the classes of the institutions. Data were collected The relationship between online risks and harm (sensed individually by the help of institutional authorities. 45-item by teenagers) varies by region in a complicated way [25]. questionnaire was used to explore the influence of Lobe, Livingstone, Ólafsson and Vodeb (2011) also demographic factors (3 items), psychological factors (12 reported, “Findings vary by child (for example, age, gender), items), social factors (12 items), religiosity level (4 items) on country and risk type, so generalizations should be treated online risks (8 items) and harm (6 items). The items were with caution” [18]. This fact was also supported by a series adapted from several studies [18, 30-33]. The constructs such of studies [26, 27]. as psychological factors, social factors and religiosity level Internet usage on a regular basis is gradually increasing were measured using a 5-point Likert scale ranging from among the people in Bangladesh. Generally, internet is Highly True (5) to Highly Untrue (1). Whereas online risks uninterruptedly available to the upper middle-class families. and harm were measured by the scales ranging from Every To maintain the status, norm, livelihood and lifestyle, both day or almost every day (5) to Never (1) and Extremely parents in these families are compelled to work from dawn to Serious/upset/angry to Not at all Serious/upset/angry (1). dusk. Consequently, their children are forced to live in their Internal consistency indexes for the constructs were

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:36-43 measured by Cronbach’s alpha; which were found .625 for with the aim of achieving research objectives. This provided emotional problems, .734 for self-efficacy, .811 for risk- the results visually and descriptively which in turn facilitated taking, .787 for parents, .864 for teachers, .808 for peers, to grasp the main trends as well as patterns clearly. After .717 for religiosity level, .93 for online risks and last of all, that, respondents’ answers were averaged to derive a mean .845 for harm. score each for psychological factors, social factors, religiosity level, online risks and harm. Finally, Pearson B. Population and sample correlation procedures were conducted on these mean scores The target population in this exploratory quantitative presenting online risks and harm with demographic factors study are the internet using teenagers in Bangladesh aged (age, gender, SES), psychological factors, social factors and between 13 and 18 years. It is mentionable that religiosity level to explore the relationships [34]. approximately all teenagers are familiar with cyber world in the developed countries. Hence, the teenagers who use internet are nearly same as the population of all teenagers in those countries. On the contrary, in the developing countries, for example in Bangladesh, this is not the case. In this region, all teenagers are not blessed with this facility. So, in this case, for whatever cause might be teenagers are not familiar with internet technology, teenagers who are using this (the population sampled for this study) are not same as all teenagers. The questionnaire was distributed among 700 urbans as well as rural teenagers in Bangladesh. Out of 700, 555 respondents returned the questionnaire back to the researcher. After screening out the incomplete responses and/or missing values, data from 443 participants were finally taken as usable responses. Hence, the response rate was 63.3%. 45.8% of the sample were male, while 54.2% were female. All the respondents were 13-18 years of old. For 16% respondents, their parents/guardians did not have even secondary level education, while for 33.6%, they had Figure 3. Pie chart of SES demography. only secondary level education. For the rest (50.4%) of the respondents, their parents/guardians had comparatively higher level of education (minimum Bachelor or above).

Figure 4. Histogram of teenagers’ Age demography. Figure 2. Pie chart of Gender demography. IV. RESULTS

This section presents the findings of the study conducted C. Data Analysis by the researcher. Respondents’ agreement to psychological Three statistical procedures were employed to analyze factors, social factors, religiosity level, online risks and harm the data. At first, descriptive statistics (percentages as well as are described at first. Consequently, the section ends with frequency counts), pie charts and histogram were employed

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:36-43 exploring the relationships of online risks and harm with all other factors.

Figure 5: Respondents’ Agreement to the items of Psychological Factors Figure 7: Respondents’ Agreement to the items of Social Factors (%). (%).

Figure 8: Respondents’ Agreement (as role of victim) to Online Risk Items

Figure 6: Respondents’ Agreement to Religiosity Level Items (%). (%).

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hazardous things for entertainment only (67.3%). Still a larger part yet a lesser level of difference was watched for other kids or youngsters pick on them (61.4%). These things demonstrated a reasonable greater part of opposing. Item with a clear majority agreeing was no matter what comes their way, they are usually able to handle it (55.3%). The items that mostly received neutral response (somewhat true) were remaining adhere to their aims and accomplishing their goals (41.3%), getting exceptionally irate and losing temper (35.9%) and frequently being miserable, pitiful or sad (35.2%). The pattern of responses shows that most respondents were confident about how to deal and/or cope with their psychological problems, yet questioned of clinging to their aims and goals, getting exceptionally irate and losing Figure 9: Respondents’ Agreement (as role of predator) to Online Risk Items temper as well as frequently being miserable, pitiful or sad. (%). B. Social Factors Fig. 7 demonstrates the respondents' consent to the items of social factors. All items received positive responses from the respondents, recording a percentage of agreement from 49% (receiving helps from peers in the past while something bothered them on the internet) to 64.1% (teachers showing them the way to use internet safely). Among them, two items received an overwhelmingly positive response from the respondents. They are their teachers demonstrating them the best approach to utilize web securely (64.1%) and in addition clarifying them why a few sites are great and some are awful (62.8%). The exceptions were parents share activities together with them and stay nearby while using the internet, which were supported by only 31.2% and 32.1% respectively. Around 44% couldn't help contradicting these Figure 10: Respondents’ Agreement to Harm (intensity) Items (%). two items. The pattern of respondents demonstrates that they are happy with the service they are receiving from their teachers while encountering internet threats. However, most doubted about their parents that they (parents) would have the capacity to help them if they (teenagers) are affected by online risks. C. Religiosity Level Fig. 6 demonstrates respondents' consent to the four religiosity level items. All items except for one got an overwhelmingly positive response from the respondents, i.e. religious beliefs influencing all their interaction with everyone (60%), carefully avoiding shameful acts (75%) and importance of following Allah’s commandments conscientiously (82.9%). However, nearly half of the respondents (47.4%) responded neutral (somewhat true) about performing their religious duties properly. In general, as appeared by the outcomes, the greater part of the respondents had positive assessments about the religiosity level they are possessing by.

Figure 11: Respondents’ Agreement to Harm (duration) Items (%). D. Online Risks A. Psychological Factors Fig. 8 and Fig. 9 demonstrate the respondents' consent to The respondents' consent to the items of psychological the eight online risks items. All items received an factors is appeared in Fig. 5. overwhelmingly negative response from the respondents, A greater part differs of doing exciting things, regardless recording a percentage of disagreement from 76.9% (Nasty of the possibility that they are unsafe (61.6%) and doing or hurtful messages were sent to me on the internet) to 87.9%

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(Someone was threatened on the internet by me). By and Under psychological factors, low self-efficacy was found large, as appeared by the outcomes, the greater part of the significantly and positively correlated with online risks (r = respondents reported negative responses about the online .416, p = .000). Statistically significant positive relationship risks they are facing with. was also found between risky behavior and practices of the respondents and the online risks they experienced (r = .251, E. Harm p = .000). Nonetheless, as for online risks, emotional Fig. 10 and Fig. 11 demonstrates the respondents' consent problem was found making no statistically significant to the six harm items. All items received an overwhelmingly differences (r = -.092, p = .054). It was also found negative response from the respondents, recording a insignificant in another study [36]. percentage of disagreement from 65% (making angry) to Parental guidance and monitoring was significantly and 72.5% (How long did you think about that for?). All around, negatively correlated with online risks that teenagers were as showed up by the results, most of the respondents were encountering in Bangladesh (r = -.218, p = .000). not bothered at all about the effect of harm generated from Statistically significant negative relationship also existed online risks. between teachers’ advice received by the respondents and the online risks (r = -.388, p = .000) as well as peers’ advice and F. Relationship between the factors and Online Risks online risks (r = -.351, p = .000). Pearson correlation procedures run to explore the Religiosity level was also found significantly and relationships between the factors (Demographic, negatively correlated with online risks the teenagers are Psychological, Social and Religiosity Level) and online facing in Bangladesh (r = -.211, p = .000). risks among teenagers in Bangladesh. Age and SES under the demographic factors; low self-efficacy and risky G. Relationship between the factors and Harm behavior and practices under the psychological factors; Pearson correlation procedures run to explore the parents, teachers and peers under the social factors as well relationships between the factors (Demographic, as religiosity level showed statistically significant Psychological, Social and Religiosity Level) and harm relationships among them. teenagers in Bangladesh are experiencing from online risks. Age and SES under the demographic factors; emotional TABLE I. RELATIONSHIP BETWEEN THE FACTORS AND problems, low self-efficacy as well as risky behavior and ONLINE RISK practices under the psychological factors; teachers and peers Summary of Correlation under the social factors as well as religiosity level showed Analysis Results between statistically significant relationships among them. Relationship between Variables the Factors and Online Risk Pearson’s r P value TABLE II. RELATIONSHIP BETWEEN THE FACTORS AND * HARM Age and Online Risk .206 .000

Gender and Online Risk -.086 .072 Summary of Correlation Analysis Results between Relationship between Variables SES and Online Risk .095 .045* the Factors and Harm Pearson’s r P value Emotional Problems and Online Risk -.092 .054 * Age and Harm .161 .001 Low Self Efficacy and Online Risk .416 .000* Gender and Harm .044 .353 Risky Behaviour and Practices and .251 .000* Online Risk SES and Harm .136 .004* Parental guidance and monitoring and -.218 .000* Online Risk Emotional Problems and Harm .179 .000* Teachers’ advice and Online Risk -.388 .000* Low Self Efficacy and Harm .234 .000* Risky Behaviour and Practices and Peers’ advice and Online Risk -.351 .000* .237 .000* Harm Religiosity Level and Online Risk -.211 .000* Parental guidance and monitoring and -.078 .100 *. Statistically significant at p<0.05 Harm Teachers’ advice and Harm -.107 .024*

The respondents’ age was significantly and positively Peers’ advice and Harm -.127 .008* correlated with online risks they were facing in Bangladesh (r = .206, p = .000). Statistically significant positive Religiosity Level and Harm -.117 .014* relationship also existed between SES of the respondents and *. Statistically significant at p<0.05 the online risks they encountered (r = .095, p = .045). However, as for online risks, no statistically significant The respondents’ age was significantly and positively gender differences were found (r = -.086, p = .072). Gender correlated with harm they were affected through online in differences were also found insignificant in a series of Bangladesh (r = .161, p = .001). Statistically significant studies [25, 35]. positive relationship also existed between SES of the respondents and harm they experienced (r = .136, p = .004).

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However, as for harm, no statistically significant gender the reasons for gender issue having no significant impact on differences were found (r = .044, p = .353). Gender online risks and harm among the teenagers in this region. differences were also found insignificant in a series of With some exceptions and doubts, the pattern of the studies [25, 37]. responses reveal that most teenagers were self-confident Under psychological factors, emotional problem was about dealing or coping with their psychological issues. found significantly and positively correlated with harm (r = Nonetheless, it is crucial to pay special attention to children .179, p = .000). Statistically significant positive relationship with low self-efficacy and psychological difficulties in order was also found between low self-efficacy of the respondents to overcome their psychological problems and develop self- and harm they experienced (r = .234, p = .000). In addition esteem. to that, risky behavior and practices was found making Though the respondents were satisfied with their statistically significant and positive relationship with harm as teachers’ as well as peers’ service while they were affected well (r = .237, p = .000). online, a greater part of them were doubted about their Statistically significant negative relationship existed parental guidance and monitoring. This lack of satisfaction between teachers’ advice received by the respondents and and confidence was echoed in the responses towards some harm they were affected by (r = -.107, p = .024) as well as items in the questionnaire. It is advisable to the parents to peers’ advice and harm (r = -.127, p = .008). However, spend more time with their children and take proper care of parental guidance and monitoring was found making no them if they get affected online rather than focusing only on statistically significant relationship with harm according to their career. their response (r = -.078, p = .100). It was also found Finally, religiosity level of the respondents had insignificant in another study [38]. significant impact on their approach towards other people Lastly, religiosity level was also found significantly and online. Although it is a critical task without a doubt in this negatively correlated with harm the teenagers are affected by advanced secularized world, religious studies ought to be in Bangladesh (r = -.117, p = .014). incorporated into expansion to typical educational curriculum so all understudies can profit by it. H. Relationship between Online Risks and Harm Pearson correlation procedure was also employed to investigate the relationships between online risks and harm. REFERENCES A statistically significant and positive correlation was [1] S. Livingstone, "Internet, children, and youth," The handbook of discovered between them as a result (r = .366, p = .000). internet studies, vol. 348368, 2011. [2] B. Albert and S. Crabbe, "The national campaign to prevent teen and TABLE III. RELATIONSHIP BETWEEN ONLINE RISK AND unplanned pregnancy and cosmogirl. com reveal results of sex & tech HARM survey: Large percentage of teens posting/sending nude/semi nude (sic) images," The National Campaign to Prevent Teen and Summary of Correlation Unplanned Pregnancy, 2008. Analysis Results between Relationship between Variables the Factors and Harm [3] A. Lenhart, J. Kahne, E. Middaugh, A. R. Macgill, C. Evans, and J. Vitak, "Teens, Video Games, and Civics: Teens' Gaming Experiences Pearson’s r P value Are Diverse and Include Significant Social Interaction and Civic * Online Risks and Harm .366 .000 Engagement," Pew internet & American life project, 2008.

*. Statistically significant at p<0.05 [4] A. Lenhart, M. Madden, A. Smith, and A. Macgill, "Teens and social media. Pew Internet and American Life Project [Internet]. Washington DC: Pew Charitable Trusts; 2007 [cited 2013 Oct 20]," ed. V. CONCLUSION [5] K. Thomas, "Teen online & wireless safety survey: Cyberbullying, Under demographic factors, age and SES were found sexting, and parental controls," Retrieved from http://www.scribd. having significant impact on online risks and harm among com/doc/20023365/2009-Cox-Teen-Online-Wireless-Safety-Survey- teenagers in Bangladesh. With increase of age, more and Cyberbullying-Sexting-and-Parental-Controls, 2009. more sophisticated technology and devices are approaching [6] S. Al-Jubayer, "Use of social networking sites among teenagers: A study of Facebook use in Dhaka City," Journal of International them, specially to the teenagers from higher SES family. Social Issues (March 2013), vol. 2, pp. 35-44, 2013. Without proper monitoring and supervision, this may make [7] T. Taher and M. A. Suhaimi, "Demographic Factors influencing them more vulnerable in the cyber world. Eventually, they Online Risks and Harm among the teenagers in Bangladesh," 2017. become more affected by online harm. [8] S. Livingstone, L. Kirwil, C. Ponte, and E. Staksrud, "In their own Previously male and female children were treated words: What bothers children online?," European Journal of differently by their parents in Bangladesh. Girls used to Communication, vol. 29, pp. 271-288, 2014. experience more restriction and control from their parents as [9] S. Livingstone and A. Görzig, "When adolescents receive sexual well as their societies. But, day by day, gender differences messages on the internet: Explaining experiences of risk and harm," are becoming less and less significant in this country. Girls Computers in Human Behavior, vol. 33, pp. 8-15, 2014. are becoming more extrovert, more educated, more outgoing, [10] S. Livingstone and L. Haddon, "EU Kids Online," Zeitschrift Für Psychologie/Journal of Psychology, vol. 217, p. 236, 2009. having more freedom and so on [39, 40]. They are not left [11] S. Livingstone and L. Haddon, "EU Kids Online: Final Report. LSE behind any more compared to boys. Both are enjoying the London: EU Kids Online.(EC Safer Internet Plus Programme same level of privilege and access to the modern Deliverable D6. 5). Retrieved on 12 January 2011," ed, 2009. sophisticated technologies and devices. This might be one of

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[12] S. Livingstone and L. Haddon, "EU Kids Online: Final Report. [26] B. Lobe, S. Livingstone, and L. Haddon, Researching children’s Report: LSE, London: EU Kids Online," ed, 2009. experiences online across countries: Issues and problems in [13] S. M. Livingstone, S. Livingstone, and L. Haddon, Kids online: methodology: EU Kids Online, 2007. Opportunities and risks for children: Policy press, 2009. [27] E. Staksrud, Children in the online world: Risk, regulation, rights: [14] S. Livingstone, A. Görzig, and K. Ólafsson, "Disadvantaged children Routledge, 2016. and online risk," 2011. [28] S. Correspondent, "Around half of country's teenagers face cyber [15] U. Hasebrink, A. Görzig, L. Haddon, V. Kalmus, and S. Livingstone, crime," in The Daily Observer, ed. Dhaka: The Editor on behalf of the "Patterns of risk and safety online: In-depth analyses from the EU Observer Ltd. from Globe Printers, 2016. Kids Online survey of 9-to 16-year-olds and their parents in 25 [29] S. O. Report, "Teenagers feel helpless to cyber-crime: Study," in The European countries," 2011. Daily Star, ed. Dhaka: thedailystar.net, 2015. [16] L. Haddon and S. Livingstone, "EU Kids Online: national [30] E. J. Helsper, V. Kalmus, U. Hasebrink, B. Sagvari, and J. De Haan, perspectives," 2012. "Country classification: Opportunities, risks, harm and parental [17] L. d'Haenens, S. Vandoninck, and V. Donoso, "How to cope and mediation," 2013. build online resilience?," 2013. [31] C. Bennett. EU Kids Online - EU Kids Online - Research - [18] B. Lobe, S. Livingstone, K. Ólafsson, and H. Vodeb, "Cross-National Department of Media and Communications - Home. Available: Comparison of Risks and Safety on the Internet: Initial analysis from http://www.lse.ac.uk/media@lse/research/EUKidsOnline/Home.aspx the EU Kids Online survey of European children," 2011. [32] (10/16/2017). Risky Online Behavior A Closer Look: Who Is At Risk? [19] A. K. Liau, A. Khoo, and P. Hwaang, "Factors influencing Available: http://www.internetsafety101.org/predatorsrisk adolescents engagement in risky internet behavior," CyberPsychology [33] S. A. Shukor and A. Jamal, "Developing scales for measuring & Behavior, vol. 8, pp. 513-520, 2005. religiosity in the context of consumer research," Middle-East Journal [20] A. Görzig and K. Ólafsson, "What makes a bully a cyberbully? of Scientific Research, vol. 13, pp. 69-74, 2013. Unravelling the characteristics of cyberbullies across twenty-five [34] J. Pallant, SPSS survival manual: McGraw-Hill Education (UK), European countries," Journal of Children and Media, vol. 7, pp. 9-27, 2013. 2013. [35] S. Livingstone, K. Ólafsson, B. O’Neill, and V. Donoso, "Towards a [21] S. Livingstone and P. K. Smith, "Annual research review: Harms better internet for children: findings and recommendations from EU experienced by child users of online and mobile technologies: The Kids Online to inform the CEO coalition," 2012. nature, prevalence and management of sexual and aggressive risks in [36] (2015, 1/11/2017). Rise in emotional problems among girls. the digital age," Journal of child psychology and psychiatry, vol. 55, Available: http://www.childnet.com/blog/rise-in-emotional-problems- pp. 635-654, 2014. among-girls [22] A. L. Hoffman, The relationship between the practice of Christian [37] P. K. Smith, J. Mahdavi, M. Carvalho, S. Fisher, S. Russell, and N. spiritual disciplines and Internet pornography use among Christian Tippett, "Cyberbullying: Its nature and impact in secondary school college students: The Southern Baptist Theological Seminary, 2009. pupils," Journal of child psychology and psychiatry, vol. 49, pp. 376- [23] W. W. Lau and A. H. Yuen, "Adolescents’ risky online behaviours: 385, 2008. The influence of gender, religion, and parenting style," Computers in [38] B. O’Neill, S. Grehan, and K. Ólafsson, "Risks and safety for children Human Behavior, vol. 29, pp. 2690-2696, 2013. on the internet: the Ireland report: Initial findings from the EU Kids [24] D. L. Murray, A survey of the practices and perceptions of students in Online survey of 9-16 year olds and their parents," 2011. one catholic high school on the use of the internet regarding safety, [39] S. M. Hashemi, S. R. Schuler, and A. P. Riley, "Rural credit programs cyberbullying, and sexting: University of San Francisco, 2014. and women's empowerment in Bangladesh," World development, vol. [25] S. Livingstone, L. Haddon, A. Görzig, and K. Ólafsson, "Risks and 24, pp. 635-653, 1996. safety on the internet: the perspective of European children: full [40] R. Heath and A. M. Mobarak, "Manufacturing growth and the lives of findings and policy implications from the EU Kids Online survey of Bangladeshi women," Journal of Development Economics, vol. 115, 9-16 year olds and their parents in 25 countries," 2011. pp. 1-15, 2015.

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(c University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Earthquake Monitoring & Early Warning System

Zaryab Qazi, Mubashir Malik, Waseem Javaid Soomro

Institute of Information and Communication Technology, University of Sindh, Jamshoro [email protected], [email protected], [email protected]

Abstract: The aim of this project is to design the system that can detect P-wave before the first S-wave spike. Typically, P-wave travel 1.68 to 1.75 times faster than S-wave. Our proposed designed device consists of a pendulum type earthquake detection device which is interconnected with fault point finder, wireless alarm, GSM kit and automatic turn off system. when P- wave strike the pendulum it activates relay and send the pulse to stimulate the wireless alarm which can be install at any place as it detects the P-waves and can save human lives as they will be aware of how to deal with this situation.

Keywords: Earthquake Monitoring, Early warning system, Earthquake warning system.;

turn off system. The huge ground vibrations from an I. INTRODUCTION earthquake are usually from the S-wave and coming after Earthquake is the vibration of earth crust due to the surface waves, which rotate at about 3.5 km/s, when P- movement of the earth plates. Earthquake occurs due to wave strike the pendulum it activates relay and send the pressure released from inside of earth layer where plates do pulse to activate the wireless alarm which can be install at not move well together with each other and occasionally any place as it detects the P-waves and can save human lives jammed. Earthquake energy spreads out in the form of as they will be aware of how to deal with this situation. As seismic waves from the focus (spot in the earth layer where we know that electronically information signal travels at tension is released). Waves closer to epicenter are most about 300,000 km/s, which is much faster than seismic powerful and as they move away from epicenter they waves, that is when, our pendulum based earthquake become less powerful. the brutal smash up of earthquake detector system detects the earthquake and sends the pulses will happen near the epicenter as shown in Figure (1) [1]. to all connected systems by using microcontroller and Different kinds of seismic waves are generated like P-wave through the GSM facility. The alerts are sent to all possible and S-wave, P-waves are harmless and faster comes first at numbers as a text message, warning the user of possible the destination and S-wave are harmful and slower than P- earthquake activity. By the using of direction meter, we can wave that reaches second at the destination. easily analyze and indicate visually that from which side it is coming either from the west or north shown in Figure (2). An automatic turn off system is also connect to turn off all possible connections such as turn off the gas, turn off the electricity to protect from the short circuit or any fire burning and open the safety doors as it detect the P- wave

Figure 1: Earth Quake breakdown at its epicenter

The aim of this project is to design the system that can detect P-wave before the first S-wave spike. Typically, P- wave travel 1.68 to 1.75 times faster than S-wave. Thus there is typically a one second separation between the P- and S- wave for every eight (8) kilometer traveled [2]. Our proposed designed device consists of a pendulum type Figure 2: Full circle division of coordinates earthquake detection device which is interconnected with fault point finder, wireless alarm, GSM kit and automatic University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:44-51

A. Primary wave (p-wave) Primary wave (p-wave) is 1.68 to 1.75 times faster than S- wave, whose rotation is about 3.5 km/s. Therefore, there is typically 1 second separation between every 8 kilometer traveled. Earthquake early warning system can utilize P- wave as source of information of earthquake. the Electromagnetic waves which is faster than P- wave and much faster than S-wave sending of early warning message is possible. The movement of primary wave power on the earth is the cycle of forward and backward shaking in a x- axis and y-axis plane, spreading in similar direction of the seismic wave. The movement of wave on the earth is the reason of the pushing (compression) and pulling (dilation) of earth elements in its path and it can pass from any type of soil structure such as solid and liquid. There are three kinds of waves Primary waves, secondary waves and in the last surface. P-wave is faster than other seismic waves. Movement of P-waves passing through the earth shown in Figure (3) [2]. Figure 4: Up and down movement of surface waves (S- Waves) C. Surface Wave Surface wave is slower than other P-wave and coming after P-wave which is secondary wave and most destructive than all types of waves. Surface wave are of two types Rayleigh an loves wave: Rayleigh waves shearing in horizontal pattern comparable to s-waves shown in Figure (5) while loves waves destructive motion in rolling type pattern like a water waves shown in Figure (6) shows the movement of Figure 3: Movement of primary waves (P-Wave) passing on both surface wave (Rayleigh and loves) along the earth [2]. the earth

B. Secondary wave (S-wave) S-wave or secondary wave also called as shear wave due to the movement of up-and-down at the right angle of earth surface. It is dissimilar with P-wave because of the movement, it can move only in solid unlike the P-wave which can also travel in liquid. Due to the movement of shear wave the element of earth causes propagate in all direction. Its velocity is slower than P-wave and between the P-waves and surface waves show in Figure (4) [2].

Figure 5: Rayleigh wave shearing movement

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1827 Lahore, Punjab 7.8 1000 1935 Alijan(Quetta), 7.7 40000 Baluchistan 1945 Baluchistan 7.8 4000 1974 Hunza, Kpk 6.2 5300 2005 Muzaffarabad, 7.8 80000 Ajk 2008 Ziarat, 6.4 215 Balauchistan 2011 Dalbandin, 7.2 NIL Figure 6: Loves waves rolling pattern movement Baluchistan 2013 Awaran, 7.8 600+ D. Aims and objectives: Baluchistan The primary aim of this project if to detect Earthquake and provide an early warning through mobile text messaging, As it can be seen from the Table 1, the earth quake in the prior to damaging ground shake of earthquake. This system region of Muzaffarabad AJK region was one of the worst can reduce the number of causalities and the cost in the natural disasters Pakistan had seen, with maximum earthquake affected areas. The GSM connectivity will be casualties. The earthquake which occurred on Saturday, used to send the alert warning messages through SMS, to all October 8th 2005 at exactly 8:2:37, was of 7.8 magnitude. by near base stations. The wireless alarm facility can also be The depth of the earthquake was found to be at 26km below incorporated into the system to warn the people which can the surface. be at any place. An automatic turn off system is also aimed, to turn off all the possible connections and open safety door in buildings, houses, offices etc. An additional key feature F. Time Estimation and Analyzing of Earthquake: using fault point finder is also included, to detect the Most people caught in earthquakes have a feeling of direction from which side the earthquake is coming. The helplessness. Especially if they have never experienced a earthquake early warning system can be installed in various quake before, they have no idea how long it is going to last different facilities and locations including but not limited to or what will happen next [5]. Every second counts to save Factories and mills, Industries, Houses, Offices, Fire the human life, therefore, initially a technical analysis is brigade’s headquarters and Hospitals. done regarding these types of disasters, determining how much time will be given to warn the people and save people, if the earthquake system is installed. First the 2005 and E. Historical Background: 2008 horrible earthquakes are analyzed, which occurred in Initially the historical background of various earthquakes Pakistan, in which heavy losses were incurred. that have occurred in Pakistan region, are analyzed. The The epicenter (origin) of earthquake which occurred in 2005 previous earthquakes, which have occurred in this country, in AJK, was at the 100 km North East (NE) of Islamabad have brought devastation and chaos as a nightmare in which city. It is necessary to keep in mind, that those areas which thousands of people had died, millions of people became are situated near the epicenter are the most affected areas of homeless and millions of were left injured. The country earthquake. As already discussed, the P-wave is information uphill bearded billions of loss during these causalities in carrier wave and it is about 1.68 times faster than S-waves, natural disaster. In this section we will discuss worst which is energy carrier or also knows as the Destructive disaster of earthquake of the history. The data provides a wave. There is about one second separation between P-wave key aspect in order to understand in detail, the various and S-wave, over a distance of every 8 kilometers per earthquake magnitudes and their patterns as given in Table traveling path. In 2005, the earthquake epicenter was 100 1. This information, will help to understand how to make km from NE of Islamabad, so there is a typically 13 seconds the public aware from such type of disaster and how to deal gap between P-wave and S-wave over 100 km travel. from this situation. Another earthquake which occurred in 2008, its epicenter was at away 600 km South-West (SW) of Islamabad. If the Table 1: Recent past earthquake and its disasters time estimation is done as previously, then it is determined that there was about 60 seconds of window between the first in Pakistan P-wave and actual destructive S-wave, and thus these 60 year Location Magnitude Deaths seconds could have been utilized to warn people and get them out of buildings and in open areas saving their lives. Suppose you have the 13 seconds to save your life, thinks

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:44-51 what you can do. You can move away from the large objects departments, and hospitals [11]. Chu-Chieh Jay Lin which may be in your house such as large shelves, mirrors developed a concept of Structural Response with On-Site large pieces of furniture topple over. If you are outside not Earthquake Early Warning System Using Neural Networks. in home you can move away from trees, signboard or from The real-time strong motion signals recorded the the large hanging objects or when you are driving you can characteristics of the sensed earthquake and stop the car to prevent any vehicle accident. “accelerograms” [12] were learned. The neural networks provide a seismic profile of the arrival ground motion instantaneously after the shaking is felt at the sensors by II. RELATED WORK analyzing the three components of the earthquake signals. By producing informative warnings, the neural network The thought to give early warning by the using of seismic based methodology has shown its potential to increase waves to coming prevent disaster is not new. In 19th century significantly the application of earthquake early warning 1856, a seismologist named Dr. JD cooper bring out the idea system (EEWS) on hazard mitigation [12]. in an san Francisco editorial column daily evening bulletin, in which he said “a various plot can be used to take different point in san Francisco between 10 to 100 miles, by this III. CONSTRUCTION OF EARTHQUAKE ALARM method an earthquake wave is high enough to destroy HARDWARE DEVICE anything can be prevent by ring the bell through the electric wires which is rotating from this city and by hang the high In this project, the following tools and techniques are used tower in the center off city monitoring can be possible” [6]. to develop and construct the earth quake early alarm system. In the middle of 20th century well known seismologist • AT89C51 Microcontroller Thomas Heaton architect a modern life view, which he • Global System for Mobile (GSM) named SCAN (seismic computerize alert network. He said • Mobile that this system can be implemented to cutoff power grids, auto turn off natural gas valves, provide possible guard to • Fault point finder protect computer system and railway lines [7]. • Visual indicator

• Wireless alarm In late 1950s, basic seismometers implement to give alarm • Automatic turn off system for warning to railway stations. After the bullet train project • Embedded Assembly and C language start in 1964, an automatic turns off system was put into • Proteus simulator operation to stop or slow the train as it detects the seismic waves. Recently used URDEAS (urgent earth quake • Serial Port Communication detection and alarm system) has a complicated algorithm for • RS 232 Protocol finding the location and waves of earthquake recently using • Power supply by the Japanese railway system. Some companies develop • Computer efficient real time ground motion detection system for quick response in emergency. Recent the city yokohama start a project to install a 150 stations network for real time A. Earthquake detector: monitoring [9]. In this project four systems are developed and interconnect with Pendulum form earthquake detector shown in Figure Hiroo kanamori, a Japanese seismologist researcher stated (7) to build up a system called GSM based early warning that “Recent advances in seismic sensor technology, data wireless earthquake alarm system. A free swing sensitive acquisition systems, digital communications, and computer pendulum is fitted inside a cylindrical shape tube, to detect hardware and software make it possible to build reliable the earthquake vibration. When seismic wave collides with real-time earthquake information systems. In the long term pendulum it sideways from its resting equilibrium position these systems also provide basic data for mitigation in cylindrical shape because of energy stored in it and it strategies such as improved building codes” [8]. oscillate like a Foucault pendulum. The four pulse receiver is connecting in bottom side of cylindrical body in all four Taiwanese seismologist Yih-Min Wu developed a Virtual direction east, west, north, south respectively. When it Subnet Work (VSN) which reduced the earthquake warning collides with any pulse receiver data acquisition process time to 30sec [10]. H. Serdar Kuyuk presented a research become complete and the receiving pulse send to all four paper in world conference on earthquake engineering, where systems which is interconnect with earthquake detector to he discussed an application of Earthquake Early Warning do perform task. Systems (EEWSs) mainly centers on alerting or providing information to the public offices such as the emergency, fire

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The GSM has a very important role here in order to warn the people. In modern world, where a mobile has become every person’s need and every individual has an access to it. The alert warning massage can be sent to people and it can save the human life as they will get early warning of earthquake and will be aware of how to deal with this situation. Wireless alarm system is a technique to warn the people through wirelessly which can be install everywhere such as room, office, building or as per need when earthquake alarm system detects the P-wave it send the signal to wireless alarm to start the alarm and it is a very useful method and quick warn technique that may save life.

Figure 7: Earthquake alarm system connected with another B. Microcontroller IC 8051: systems In this project AT89C51 microcontroller is used. The microcontroller has a major role, as it is the brain of the After recognizing the seismic wave direction, it will send system. Here the use of microcontroller is to connect the information to visual Indicator, the function of visual four individual systems with the microcontroller to work indicator is it visually shows the warning from which point simultaneously. The microcontroller is used to communicate seismic wave is coming either from the east or the west. The with all four devices, pin number from 21 to 24 is used for automatic turns off system has a very key role in modern visual direction meter and pin number 27 to 28 is used for system in which every single device is automatic. When the wireless alarm and automatic turn off system. Pin number P-wave strikes with pendulum it sends the pulse to the base 10 to 11 is reserved for max 232 for interface purpose and station, trigging an alarm to automatically turn off the pin number 1 to 4 is used for pendulum pulse receiver and system. In the late 1954 automatic turn off system is pin number 5 is used for system reset. Pin number from 18- installed in Japanese railway station to stop or slow down 19 11.059 MHz oscillator is connected and pin number 20 the train. It can be used in atomic reactor plant, industries went ground and pin number 40 is connected to 5-volt and where the people working with heavy machinery, power supply. The detailed circuit diagram of the system is especially where casualties may be occurring. shown in Figure 9.

Figure 8: Block diagram

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:44-51

the early warning service. on the other hand, wireless alarm system which can be install at any place start alarm as it receives the alarm signal and by this action people can be aware that earthquake is coming and it may save the life as they will aware that how to deal with this situation. The automatic turns off system has a very important role in modern life where every signal device or system is automatic. As it receives the warning signal it quickly turns off the all possible domestic connection such as it turns off the electricity and gas for preventing any short circuit or fire burning due to leakage and open safety/ emergency door and in industry level it can turn off the machinery or any system to prevent any damages and start alarm to move away worker from heavy machinery may reduce the casualties. In the end visual direction indicator is used to visually show the direction that from which point earthquake is coming either from the east or west. This all system working quickly at the same time as it is receiving the pulse and performing early warning operation through the rapid action has a role of saving the human life is made possible by the use of microcontroller.

Figure 9: Circuit diagram

C. Construction: Designing of this project is possible by using of AT89c51 microcontroller. The microcontroller has a central role in this project to design a GSM based early warning earthquake alarm system. The earthquake alarm system is interconnecting with four another system, this made possible by the use of microcontroller to work simultaneously. The circuit diagram of the system is shown in Figure 9. A free swing sensitive pendulum fitted in cylindrical shape to detect the earthquake vibration as shown in figure 10. When seismic wave collides with pendulum it sideways from its resting equilibrium position in cylindrical shape because of energy stored in it and it oscillate. The four pulse receiver is connecting in bottom side of cylindrical body in all four direction east, west, Figure 10: Free swing pendulum fitted in north, south respectively. When it collides with any pulse cylindrical body receiver data acquisition process become start and the receiving pulse send to all four systems which is interconnect with earthquake detector to do perform task. r. The microcontroller as receive the pulse from the pulse receiver it sends the warning pulse at the same time to four another system which is interconnect with earthquake system. when GSM kit receive the signal it sends the warning alert message to all mobile number which is stored in database of GSM, and may save the life by performing

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institution to the people before the earth quake destruction then early warning concept fulfill. The proposed system methodology gives big contribution to making a efficient system with the usage of GSM for broadcasting the early warning message and wireless alarm for start alarm as it receive the signal and automatic turn off system for turn any system to prevent any damages during working. The working of a system is made possible by the using of AT89C51 microcontroller which is the heart of the system and gives instruction to all the connected system for work simultaneously and to give the whole package of Earthquake detector and people warn system

V. CONCLUSIONS In this study, Real time prediction and alarm system to warn the people has been accomplished. The main key problem to warn the people in real time mode is tried to solve. Using the GSM system, an early warning message sent as the earthquake detector detects the earthquake sound wave and generate the pulse to auto turn off system that would turn off the gas or electricity connection and send the pulse to alarm Figure 11: Construction progress system to warn the people using voice audible alarm. By analyzing the previous earthquakes we D. Software Used: reached at this point that casualties can be This chapter consists of information about software which is minimize if the people get earthquake coming used in this project. There are many electronic software authentic information in real time to take action which is used for designing and simulating the electronic before the coming of destructive sound wave. The circuit. GSM based early warning earthquake alarm system proposed system has a key role to make the useful has a very important role to save the life in natural disaster. To design a efficient earthquake alarm circuit it is very earthquake system to warn the people in real time important to design a circuit correctly that’s why we are and send the message to the people as it receive using some of following software to accomplishing the the earthquake initial sound wave and people can GSM based early warning earthquake alarm system. take action before the coming of destructive wave. We used multisim software to check circuit through simulation. Micro Vision keil is used to convert the C language program into the HEX file and burned it into VI. REFERENCES microcontroller IC. Express schematic is used to draw the diagram of the circuit and visual basic used for GSM [1] Earthquake natural Hazards (online) Available: interfacing purpose and for making the earthquake http://www.bbc.co.uk/schools/gcsebitesize/geography/ monitoring application. natural_hazards/earthquakes_rev1.shtml

[2] Giuseppe Olivadoti “Sensing, Analyzing, and IV. RESULTS AND DISCUSSION Acting in the First Moments of an Earthquake” In this study, one of the most key problems occurs in real time prediction of earthquake system is tried to solve. This [3] DOKTORS DER NATURWISSENSCHAFTEN thesis proposed a new view point to add communication “Real-time Information from Seismic Networks” resources and others previous research in order to make a one new efficient system to warn the people in real time. By [4] Pakistan earthquake [online] Available: analyzing of previous earthquake record we reached at this www.thepakistanquake.com point that life losses can be minimize if the authentic earthquake information reached from the identified

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[5] earthquake safety [online] Available: [15]Enineering garage [online] Available: http://www.scec.org/education/k12/tremortroop/Unit5. http://www.engineersgarage.com/contribution/intellige pdf nt-ambulance-automatic-traffic-control?page=2

[6]ieee spectrum [online] Available: [16] L-com Globel connectivity [online] Available: http://spectrum.ieee.org/at-work/innovation/a-brief- http://www.l-com.com/customer-service?ID=4878 history-of-earthquake-warnings Technical Report [7] Georgia Cua Thomas Heaton “The Virtual [17] Vardakas, John, “Twists and Turns in the Seismologist (VS) method: A Bayesian approach to Development of the Transistor IEEE-USA Today's earthquake early” Department of Civil Engineering, Engineer” May 2003. California Institute of Technology, Pasadena, USA [18] Informit trusted technology source [online] [8] Yutaka Nakamura “Uredas, urgent earthquake Available: detection and alarm system, now and future” 13th http://www.informit.com/articles/article.aspx?p=39227 World Conference on Earthquake Engineering 8 Vancouver, B.C., Canada August 1-6, 2004 Paper No. 908 [19] Keil tools by ARM embedded development tool [online] Available: http://www.keil.com/ [9] Hiroo Kanamori, Egill Hauksson & Thomas Heaton “Real-time seismology and earthquake hazard [20] Express Pcb designing tool [online] Available: mitigation” Nature Macmillan Publishers Ltd volume http://www.expresspcb.com/expresspcbhtm/Free_sche 390 4 December 1997 matic_software.htm

[10] Yih-Min Wu and Ta-liang Teng “A Virtual [21] Rimu Pcb [online] Available: Subnetwork Approach to Earthquake Early Warning” http://www.hutson.co.nz/rimupcb.htm Bulletin of the Seismological Society of America, Vol. 92, No. 5, pp. 2008–2018, June 2002

[11] H. Serdar Kuyuk and Masato Motosaka “spectral forecasting of earthquake ground motion using regional and national earthquake early warning systems for advanced engineering application against approaching miyagi-ken oki earthquakes” The 14th World Conference on Earthquake Engineering October 12-17, 2008, Beijing, China

[12] Chu-Chieh Jay Lin, Zhe-Ping Shen & Shieh- Kung Huang “Predicting Structural Response with On- Site Earthquake Early Warning System Using Neural Networks” Proceedings of the Ninth Pacific Conference on Earthquake Engineering Building an Earthquake-Resilient Society 14-16 April, 2011, Auckland, New Zealand

[13]Slide share [online] Available: http://www.slideshare.net/victerpaul/4-gsm-network

[14]SL group technical manual [online] Available: http://www.sl113.org/wiki/Electrical/Relays

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ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Assessing ICT Implementation and Acceptance at Public Sector Universities in Pakistan 1Shahmurad Chandio, 2Muhammad Sadry Abu Seman, 3Suhaila Samsuri, 4Abida Kanwal, 5Asadullah Shah

1345International Islamic University, Malaysia, department Information system kl, Malaysia 4International Islamic University, Malaysia, department of library & Information science kl, Malaysia [email protected], 2,[email protected], [email protected], [email protected], [email protected]

Abstract: This paper has assessed the implementation and acceptance of information and communication technology (ICT) at public sector universities in Pakistan. This research was conducted in three major public-sector universities based in one province of Pakistan. A total of 550 questionnaires were distributed and 325 were returned. In addition, three interviews were conducted from the administrative personnel of universities. The research was conducted to assess ICT implementation at universities and its acceptance by academicians. The base for this research was an integrated framework developed for this research called academicians acceptance and use of ICT (AAU-ICT). This framework was developed using; Unified theory of acceptance and use of technology (UTAUT) with an additional four constructs Culture, external incentives, perceived needs and job relevance. In order to make the results strong, a mixed method approach was adopted by incorporating both quantitative and qualitative methods. The results demonstrate that ICT is being implemented at universities, yet it has issues of equipment availability and proper environment, while academicians’ acceptance of ICT is more contingent on to their field of teaching than the need of ICT in profession.

Keywords: ICT implementations, AAU-ICT Framework, academicians, universities, Pakistan;

education was started in decade early 90’s while universities I. INTRODUCTION were provided with computers and internet, since then ICT In the 21st century, information and communication implementation at universities remain continue and several technology has changed the traditional ways of living and advance project were also announce and adopted like high work environments. Thus, the digital era, has brought every speed Internet, sophisticated computer labs, ICT equipment’s sector of life on ways and mediums provided by technology. including computers and video conference rooms at each Hence, education system around the globe have integrated university as well as digital library access. Surprisingly, all ICT system, from primary to upper levels. In actual fact the these efforts for ICT adoption have not be so successful, with developed world has completely transferred its education most university environments still fixated with manual work through the use of technology, while developing nations are environments. Classes are still not digitized, there is no central in the implementation phase. The state of any education record management system and libraries are still manually system is determined by the quality of its higher education catalogued. According to [2] various educational policies has system (HES), because the HES contributes to the been made but none accomplished successfully. Moreover, development of education at all levels[1]. In addition, the faculty members especially those in other than ICT related UNESCO considers that “ICTs can contribute to universal subjects, are still striving to accept ICT in their academic access to education, equity in education, the delivery of work. Pakistan is trying to make its universities more like the quality learning and teaching, teachers’ professional modernized and high-class universities of the developed development as well as improve education management, world. Efforts still are needed to be on a par with international governance and administration”. Hence, universities are the standards, not only in implementation but also ICT utilization. actual source of providing higher education. Therefore, this Hence, few studies have been conducted, but still no study has been carried out to evaluate implementation and comprehensive research focusing on the implementation level acceptance of ICT at public sector universities of Pakistan. and finding out critical factor for the successful use of ICT has Pakistan being a developing nations, always had a vision to been carried out. Therefore, this research was carried out to bring improvements in every sector of life and to be like know the implementation level at different universities, while developed nations, meanwhile government have always sorting out the factors that could have an impact on academic proposed plans to advance and adopt technology in every staff for acceptance of ICT in their academic work. sector, by realizing the higher education being the most . important base to be used for main human resource who can face new challenges of this ICT era, thus, ICT degree University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg: 52-56

II. RESEARCH OBJECTIVES ICT infrastructure and readiness in generally become a challenge for developing countries like Pakistan [14] a) To know the ICT implantation at public sector Therefore, this research was carried out in education universities. perspective with focus on public sector universities in b) To know the acceptance and use of ICT at public sector province, of Sindh, Pakistan. The approach used to assess ICT Universities. implementation and acceptance in universities used a c) To know the hindrances in successful adoption of ICT. framework developed on comprehensive review of literature, d) To know the critical success factors in the acceptance based on latest theory UTAUT [15] and some additional and usage of ICT. factors. The framework is discussed and given in next section. . IV. FRAMEWORK USED FOR THIS RESEARCH III. LITERATURE BACKGROUND The framework used for this research is developed using Technology implementation, adoption acceptance and UTAUT model along with some additional constructs called usability have been discussion of many researchers for many Information and Communication technology academicians decades. in the span of time, many theories and models have acceptance (AAU-ICT) [16]. been developed for assessing the acceptance of information . and communication technology. Now it has become perceptible that the key factor in success of any innovation is education system and academicians. Hence, numerous studies have been conducted worldwide on technology acceptance. The literature shows that the, Theory of reasoned action (TRA), Theory of Planned behavior (TPB), Technology Acceptance Model (TAM), and one of the latest theory, the Unified Theory of Acceptance and Use of Technology (UTAUT) are also used to assess ICT adoption in educational institutions and ICT acceptance and use by academicians. The UTAUT was originally proposed [3], where all previously established eight models were combined and a comprehensive theory was developed. Now the theory UTAUT is considered the most dominant in information system field [4]. This research was also carried out based on UTAUT, along with Figure 1. Framework AAU-ICT additional factors that includes culture, external incentives, perceived needs and job relevance. These factors are added The root constructs and additional constructs used in the due to different contexts and approaches for this research. framework are defined below. The Literature in context of the ICT adoption clearly shows the gap between the developed countries and developing BI- level to which an individual intends to perform a nations [5]. In a [6] describes that despite of vast impact of specific behaviour [3] ICT, the inequitable to access it, still remains the major issue. Use Behavior- The actual behavior of user measured The different studies show that every country has its own through frequency of use [3] culture and the studies demonstrates that that national culture Culture- the impact of prevailing practices on an does influence the ICT adoption in different nations [7]. In individual [17]. Culture was measured through addition, it provides to new environment in an organizations the factors stated in [18], consisting Power [8]. The ICT in higher education brings quality and provides Distance, Individualism / Collectivism, Uncertainty modern approach to communicate and process. Nowadays, it Avoidance, Masculine / Feminine is considered too of collaborative learning [9]. Furthermore, EI- impetus to incite an action in an individual [19] ICT provides the most influential way to internationalize PN- Level of Improvement in performance [20] higher education. Hence, Pakistan too need to internationalize JR- The application of intended system in an individual’s ICT curriculum and approach to utilize ICT [10]. However, Job [21]. Pakistan introduced ICT in its higher education institutes in PE - is the extent to which an individual believes the early 90’s, since than many projects has been initiated, almost system will help them do their jobs better [3] basic infrastructure is available at every HEIs [11] but the EE - related to how easy an individual believes the system studies shows that despite of various efforts there is low usage is to use [3]. of ICT, no upgradation in infrastructure and lack of SI – Individual’s intention to use the system by inspiring maintenance [11], [12]. According to the global information others [3]. technology report 2016 by world economic forum Pakistan is FC - Organization resources available to individuals to use ranked at 110th position of out of 143 countries in networked the system[3]. readiness index [13],while latest report about in the ICT readiness index shows Pakistan position at the 111th [14]. The

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V. RESEARCH METHODOLOGY 83 25.53 % This research utilized both approaches i.e. quantitative and Wi-Fi qualitative by adopting an explanatory research strategy [22]. Data collection was achieved using the survey method for a quantitative approach based on stratified sampling technique Object 2. To know the acceptance and use of ICT and face to face interview method based on expert sampling by academicians at public sector Universities for both quantitative and qualitative approaches, respectively [23]. A total of 500 questionnaires were distributed among The second objective was achieved by exploring the the faculty in the first phase of this research consisting of a response from the academicians as shown in table 2. That survey questionnaire based on quantitative research. The explained that there is acceptance and usage of ICT, although qualitative part comprised of interviews was conducted from not in ideal position, yet much more effort is needed to put three different experts working in different public-sector ICT at the top, as is the case in modern world universities. The universities. These Interviewee were selected based on expert results show that the usage of available resources by sampling technique. academicians is up to 62% while 38% of respondents said they use ICT resources at a low level. In addition, the level of VI. RESULTS Computer acquaintance demonstrates that most of the The Results achieved based on analysis are described respondents are average with 73% percent, while there are according to the objective of the research also people with very basic knowledge of ICT comprising 7%. Object 1. To know the ICT implantation at public In addition, the percentage of respondents at advanced and sector universities expert levels is 20%, This data shows that, there is high In order to achieve this objective, part two of the survey acceptance of ICT by academicians at universities, while a instrument was provided with items, to know the facts and shortage of resources may be a hindrance in using the figures about ICT availability at the universities. That part was available resources. comprised of queries about the implementation of ICT or facts Table 2: Acceptance and use of ICT about ICT availability at the universities. This object Response examined the extent to which the ICT infrastructure is in Attribute rate Percentage All available 77 23.69. % practice at various public-sector universities. The table 1 resources Usage of ICT shows the details of ICT implementation at public sector Most of the 124 38.2% facilities universities. It is this revealed that the basic infrastructure available resources available tools are already available but not equally distributed among Few of the 83 25.5% various departments nor equally provided, yet awareness available resources seems to be an issue due to a lack of training facilities. While Very few of the 41 12.6% the Internet is the major service provided, all other supporting available resources and modernized equipment for ICT are more or less not Basic 22 6.8% available in to 50% of departments, in addition ICT is being Average 238 73.2% used individually but no integrated campus management Level of Computer system is implemented, which is compulsory to modernize the Advance 50 15.4% literacy working environment. Moreover, classes are not digitized yet as there is 49% response for the availability of Multimedia Expert 15 4.6% projects at different departments, even though software availability is only availed by 27%., while Wi-Fi is only availed by 25.53%. Object 3. To know the hindrances in successful adoption Table 1: General Implementation of ICT of ICT Variable Items Frequency Percentage Internet 312 96 % This objective was achieved by knowing the characteristics Computer labs 186 57% that can act as hindrances or impediments to success in ICT. Software 89 27 % The data given in table 3, demonstrates that equipment Digital library 165 51% access availability is the major hindrance in the successful adoption Video Conference of ICT, as it, was notified by 49% of the respondents that there 75 23 % Availability of System is a lack of equipment. while other hindrance were provision ICT of trainings and technical support with percentages of 24% Multimedia 159 49% equipment System and 11%. In addition, there were other issues mentioned such Campus 4 as environment 6%, cost 4%, time 5% and interest 1%. Management 1.2% Information System

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Table 3: Hindrances in ICT adoption it was thus confirmed that the factors are critical to be Attribute Response rate Percentage considered for successful acceptance of ICT systems by their Equipment availability 158 48.6% users especially academicians. Training 78 24.0% Time 15 4.6% VII. DISCUSSIONS Interest 5 1.5% Technical support 37 11.4% The results show that the implementation of ICT at Environment 19 5.8% universities is at the middle phase. The ICT systems are Cost 13 4.0% incorporated but despite three decades of implementation, all ICT related equipment’s, services and applications are provided in average, hence use age of them is of average level. Object 4. To Confirm the critical successful factors in Meanwhile, Integrated system is not implemented, the acceptance and usage of ICT universities sampled in this research don’t us even campus management system. In can be said that, there is lack of ICT The Factors stated in framework given in Fig 1, were environment in universities, things are parallel with manual statistically measured per the scale used in the instrument. The physical and computerized approaches. Moreover, the ICT reliability analysis results yield the results that factors are acceptance and use by academicians is also different in important to be retained in model. Therefore, based on the different departments. ICT relevant departments are more acceptable range of results for reliability this shows that these efficient in acceptance while others need motivation programs factors have somehow influence on behavioral intention of and facilities to use it. In addition, there are enormous individual towards use of ICT, the reliability results are stated hindrances, such as lack of equipment and trainings and way in table 4. of communication by management, alongwith technical help. Table 4: Reliability Analysis Furthermore, the results for the objective four have been S. Constructs Reliability supportive to the proposed factors. Therefore, it can be No assumed that this research has identified critical successful 1 Behavioral Intention .952 factors to be looked in to for the proper success of ICT implementation as well as acceptance by academicians. 2 Use Behavior .902

3 Power Distance .942 VIII. CONCLUSION 4 Individualism/Collectivism .825 5 Uncertainty Avoidance .906 This research paper has focused on ICT implementation in universities and its acceptance by academicians in universities 6 Masculine / Feminine .928 in Pakistan. The results show that ICT is being implemented, 7 External Incentives .902 but there is still a lack of proper infrastructure, equipment, training and environment. The other side of the research 8 Perceived Needs .801 pertaining to academicians revealed that academicians 9 Job Relevance .741 consider ICT a useful tool to be used for quality education, 10 Performance Expectancy .933 meantime lack of resources, awareness, trainings and 11 Effort Expectancy .965 environment are challenges for its effectiveness. In addition, this research contributed by exploring success factors for ICT 12 Social Influence .889 acceptance and usage by academicians, which lead to reveal 13 Facilitating Conditions .820 academician’s acceptance and use of ICT framework. Furthermore, this research finding provides ICT status and Furthermore, the factor extraction was also conducted to potential implications for improving ICT infrastructure and determines the smallest number of factors used to represent teaching standards. Especially, factor like incentives and the best interrelationship among the set of variables by facilitating conditions could be the most important ones to adopting Kaiser ‘s criterion and using the Principal successful adoption of ICT by academicians, while training component analysis method from SPSS. According to and awareness programs can stimulate the perceived need to Kaiser’s Criterion or the eigenvalue rule, only factors with use ICT and raise the awareness of ICT’s potential benefits. eigenvalue of 1.0 or more are retained for further investigation In return academicians can motivate themselves for [24] relatively the same factors which were found acceptable performance expectancy and effort expectancy regardless of in the reliability test generated results of an eigenvalue >1. this field relevance. Whereas focusing on ICT acceptance by credence to strength to research that the factors are important. academicians can enhance culture factors could be Meanwhile, the total variance explained by the first factor was effectiveness to bring ICT based working culture in found to be 23.564, while the remaining variance in the model universities, of developing country like Pakistan. Importantly was explained by 13 other factors. this research validated the four principal factors of the UTAUT model in the context of a developing country’s educational system. Finally, the findings of this research are beneficial to both the university staff, top management, higher

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg: 52-56 education commission, ICT policy makers and government of [10] A. Saleem and K. Higuchi, “Globalization and ICT innovation policy: Pakistan. Especial recommendations based on finding are all Absorption capacity in developing countries,” Int. Conf. Adv. academicians working in universities regardless of their field Commun. Technol. ICACT, pp. 409–417, 2014. [11] Z. A. Shaikh, “Role of ICT In Shaping the future of Pakistani Higher of specialization should be provided with mandatory training Education System,” Turkish Online J. Educ. Technol. 10(1), 149-161., on ICT potential benefits and its proper utilization for vol. 10, no. 1, pp. 149–161, 2011. academic excellence. Universities should be integrated with [12] M. Bakhsh, A. Mahmood, and N. A. Sangi, “An assessment of campus management information system and top students’ readiness towards mobile learning at AIOU, Pakistan,” 2015 management should especially concentrate on communication Int. Conf. Inf. Commun. Technol. ICICT 2015, 2016. through online resources, which would bring all staff on ICT [13] World Economic Forum, The Global Information Technology Report based services and will be motivated to use ICT effectively. 2016 Innovating in the Digital Economy. 2016. Finally, the government should increase its budget for [14] Klaus Schwab, “The Global Competitiveness Report,” World universities, so, that they could adopt modern technology as Economic Forum, 2017. per their requirements. [15] Y. K. Dwivedi, N. P. Rana, A. Jeyaraj, M. Clement, and M. D. Williams, “Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model,” Inf.

IX. FUTURE WORK Syst. Front., pp. 1–16, 2017. [16] S. Chandio, M. Sadry, S. Samsuri, and A. Shah, “Acceptance and Use This research scope was limited to one province in of Information and Communication Technology by Academicians : Pakistan. Therefore, further research is needed from all Towards A Conceptual,” 2016. provinces of Pakistan, to achieve comprehensive view of ICT [17] A.Zakour, “Cultural differences and information technology implementation and acceptance at higher educational acceptance,” in … Conference of the Southern Association for Information …, 2004, pp. 156–161. institutes of Pakistan. In addition, this paper has focused on [18] G. Hofstede, Culture’s consequences: International differences in facts about ICT implementation in target universities while work-related values. Sage, Beverly Hills, 1980. basic statistics was performed to know the validation of [19] E. A. Locke, “Toward a theory of task motivation and incentives,” proposed constructs in Framework. Though the results Organ. Behav. Hum. Perform., vol. 3, no. 2, pp. 157–189, 1968. strongly support the determinants in both quantitative and [20] W. Fatimah, W. Ahmad, A. G. Downe, and T. T. Lai, “Determinants qualitative results, that too achieved the objectives of this of Computer Usage among Educators,” in Institute of Electrical and paper, Meanwhile, an advance statistics like structural Electronics Engineers, Universiti Teknologi PETRONAS, NPC, & Equation modeling (SEM) and hypothesis testing will be National Postgraduate Conference. (2011). 2011 National Postgraduate Conference: (NPC) ; 19 - 20 Sept. 2011, Universiti Teknologi performed in future. PETRONAS, Bandar Seri Iskandar, Tron, 2011, no. 2. [21] V. Venkatesh, “Technology Acceptance Model 3 and a Research REFERENCES Agenda on Interventions,” vol. 39, no. 2, pp. 273–315, 2008. [1] M. W. Nisar and S. A. Munir Ehsan Ullah Shad, “Usage and Impact of [22] S. Uma and B. Roger, Research Methods for Bussiness, 6th ed. Wiley, ICT in Education Sector ; A Study of Pakistan,” Aust. J. Basic Appl. 2014. Sci., vol. 5, no. 12, pp. 578–583, 2011. [23] J. W. Creswell, Research Design Qualitative, Quantitative, and Mixed [2] W. Abbas, M. Ahmed, R. Khalid, and T. Yasmeen, “Analyzing the Approaches. 2009. factors that can limit the acceptability to introduce new specializations in higher education institutions: A case study of higher education [24] P. Dunn, “SPSS survival manual: a step by step guide to data analysis institutions of Southern Punjab, Pakistan,” Int. J. Educ. Manag., vol. using IBM SPSS,” Aust. New Zeal. J. Public Heal., vol. 37, no. 6, pp. 31, no. 4, pp. 530–539, 2017. 597–598, 2013. [3] Viswanath Venkatesh et.al, “User acceptance of information technology: Toward a unified view,” MIS Q., vol. 27, no. 3, pp. 425– 478, 2003.. [4] M. D. Williams, N. P. Rana, and Y. K. Dwivedi, “The unified theory of acceptance and use of technology (UTAUT): a literature review,” J. Enterp. Inf. Manag., vol. 28, no. 3, pp. 443–488, 2015. [5] S. Kyakulumbye, M. Olobo, and V. Kisenyi, “Information Communication Technology ( ICT ) Utilization in Private Universities in Uganda : Exploring Strategies to Improve . A Case of Uganda Christian University,” vol. 2013, no. February, pp. 22–29, 2013. [6] B. Swar and G. F. Khan, “Mapping ICT knowledge infrastructure in South Asia,” Scientometrics, vol. 99, no. 1, pp. 117–137, 2014. [7] A. A. Erumban and S. B. de Jong, “Cross-country differences in ICT adoption: A consequence of Culture?,” J. World Bus., vol. 41, no. 4, pp. 302–314, 2006. [8] S. Majumdar, “Emerging Trends in ICT for Education & Training,” Dir. Gen. Asia Pacific Reg. IVETA, 2002. [9] N. Duţă and O. Martínez-Rivera, “Between Theory and Practice: The Importance of ICT in Higher Education as a Tool for Collaborative Learning,” Procedia - Soc. Behav. Sci., vol. 180, no. November 2014, pp. 1466–1473, 2015.

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ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Estimation of Absolute Speed of Vehicle with the Simplified Inverse Model

Seher Zamir1, Syeda Sumbul Zehra Naqvi1, Syeda Hira Fatima2 1 Department of Electronics, Mehran University of Engineering & Technology, Jamshoro 2 Institute of Mathematics and Computer Science, University of Sindh, Jamshoro

[email protected], [email protected], [email protected]

Abstract: Presently the calculations of complete speed of railway vehicles are very useful for the people in Pakistan. This is obtained by observing the stimulated time shift for the arbitrary track between two wheel sets of a bogie. The model developed in this research work is aimed to collect specific characteristics of the movements at the wheel sets via two inertial sensors seated onto a bogie. An input in the system is provided by means of the rough surface of track that produces bounce in both wheels that is identical except in the delay of time. This delay of time is detected by the space between the two wheels. The cross-correlation calculation technique has been used to determine the delay in time between the movements from one to another. The calculating method is achieved by extraction, which is based on a discursive model of vehicle.

Keywords: Cross Correlation, Simplified Inverse Model, Time delay

vehicle. Filter is used to sense the speed of the vehicle and I. INTRODUCTION fuzzy logic is implemented for this. Basically, three types of The rail vehicle speed’s measurement is often conquered sensing filters are used to reduce high frequency noise of the by noticing the speed of revolution of the wheels, as the acceleration calculations and the error is also measured that width of the wheel is static. This standard practice has been is coming between the vehicle speed [4][5] discussed that providing acceptable accuracy for a long time because there for the development of modern vehicle, its safety and is no extreme possibility for the wheel slide; this is a traction control are the facts of under consideration. The process that allows a wheel to lag and to overrun for the presented work in this paper is based on Composite station of trains. This technique will be employed for the Nonlinear Feedback (CNF) controller application for the modeling and simulation of speed of the railway vehicles. controlling strategies of vehicle lateral dynamic behaviour This research is based to study the up to date data available based on direct yaw moment compensation. Basically, for for the estimation of railway vehicle ground speed and to the model of vehicle, two models are constructed one for the acquire a detailed model of vehicle by using one of the most non-linear and the other one for tire. All the controlling extensive methods of multimode packages of simulation, performances have been conducted by means of numerical mostly used by the manufacturers of vehicle and to contrast simulations using MATLAB/Simulink platform. The main with the International Union of Railways (UIC). Finally, the factors for roadway usage, specially by large vehicles is model ought to be able to calculate the speed. The chief discussed to evaluate the infrastructure of highway lifespan, principle of this research work is to study and estimate the for this purpose, non-conventional techniques are simulation of the measurement of the absolute vehicle speed specifically designed for the purpose of calculating the and finally analyze the simulated results. speed of vehicle by means of single loop detectors [6][7] present the calculations of the speed of vehicles based on grey constraint optical flow algorithm. A vital role is played II. STATE OF THE ART by the vehicle speed measurement for Intelligent [1] presents dependable and appropriate measurements of transportation systems (ITS). The speed of vehicle basically absolute vehicle speed by estimating the time shift of two can be measured in two ways i.e. via hardware- based adjacent wheels’ motion via cross correlation which is vital methods or software-based methods. In this paper, a for control purposes of vehicle tracking. [2] shows an software based method is implemented to estimate the speed approach for the fault control tolerant for the vehicles of any vehicle by video images. A loop detector named” connected to an electrical independent four-wheel driven Induction-coil loop speed measurement” is embedded into system also for that a controller is prepared for ensuring the the ground as whenever a vehicle goes across this loop to stability of vehicles. [3] used an accelerometer and wheel detect the speed. Also, a laser detector is used to be placed based speed sensor to accurately estimate the speed of the

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above the roadway to detect the distance of moving objects and their speed. [8] Presented a fault tolerance approach that is done without any use of redundant actuators to offer stability for various failure modes. [9] suggests a new technique for the calculation of speed of train by using inertial sensors mounted onto a bogie. The suggested method estimates the speed of time shift for the continuous movement for wheels that is obtained from the behaviour of a railway bogie to observe the excitations that Figure 2 Movement of wheels due to track roughness. always occur in railway tracks. Figure 3 below provides a method for the calculation of [10] Presented to accomplish the control approach for the the speed of the vehicle. Two inertial sensors are mounted various speed tracking control systems. The used method for on the bogie frame to perform pitch and bounce dimensions. estimating the control functions of brake and drive is done An inverse model filters are produced that work as by means of an algorithm named” Switching algorithm” observers, after that output signals are merged together to without calibration, which is also connected with the model present the assessed upright movements of the two sets of of inverse longitudinal vehicle and adaptive regulation of the wheel, (A1 and A2). After that, cross-correlation MPC which is used as engine brake torque for the variety of measurements are done of these merged signals that are driving conditions and to pass up high frequency forwarded to the moving windows, that helps to estimate the oscillations repeatedly. delay in time of the two wheel sets, the result is further

utilized to measure the absolute speed of the vehicle by the

help of given equation: III. PROPOSED WORK Vm = 2Lb/Tdelay (1)

A simple railway bogie is given in Figure 1, consisting of two wheel sets that are joined frame with the help of primary suspensions. Then the secondary suspensions link this frame to the vehicle body. This Fig. is the key to the development of the model for the proposed work.

Figure 3 Speed measurement scheme.

The vertical dynamics of a vehicle included lateral, yaw and longitudinal motions are the concerned parameters for the accurate speed of the vehicle, however this paper neglected the above-mentioned parameters and only two parameters are considered, that are the acceleration and pitch motions for the speed of the vehicle

Figure 1 Schematic of a bogie. IV. PERFORMANCE EVALUATION

All the parameters influencing on the performance of the The key purpose of the design of the filter is to extract the wheel set of train caused by the roughness of the track are delay in time between the two sets of the wheels. This is exactly same; the only parameter that varies is the time elaborated in Figures 4 and 5, setting the initial velocity of delay between the two wheels as shown in Figure 2. The the train to be 183.67 km/h in the simulation. determination of this delay is calculated with help of the distance between the wheels and its own speed. Thus, the time delay will be the only parameter that will help in gathering the exact speed measurement of the specific vehicle.

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Figure 7 Resulting Cross correlation of the productive motions at 46, and Figure 4 Signals from sensor at the velocity of 183.67 km/h. 95.32 km/h

V. OUTCOME OF THE PROPOSED PROTOTYPE

Figure 8 illustrates the increase in speed and then decrease in speed of the train. Whereas, the error is measured in Figure 9. From these measurements, it can be stated that the model is capable to estimate the real velocity of the vehicle, while the estimated error is less than 6 km/h, which is mainly due to a frequent deceleration.

Figure 5 Signals out from the filters. The cross-correlation calculations of the signals from Figures 4 and 5 are shown in Figure 6. The maximum amplitude signifies delay (Td) as: Td=52ms or 51.5ms if Ts=0.51 ms, where Ts is the sampling interval. Using (1) we get the speed of vehicle as 50 m/s (at Ts= 1.0 ms) or 50.38 m/s (at Ts= 0.51 ms).

The errors of 1 m/s (1.960%) and 0.521 m/s (1.021%), respectively in the calculations, are mainly due to limited samples. Two more speeds of the vehicles have been Figure 8 Increase and decrease in speed extracted from the calculation of cross-correlation by setting two different sampling intervals as Ts=1ms and Ts=0.5ms as shown in Figure 7. That gives the speed of the vehicle to be 46 km/h (12.5 m/s) with a delay of 209ms and 95.32km/h (26.2m/s) with a delay of 100ms respectively.

Figure 9 Error in acceleration and deceleration

VI. CONCLUSION

Figure 6 Resulting Cross correlation of the productive motion at 183.67 In this paper, an easy implementation technique has been km/h used to serve a productive calculation of the railway vehicle speed for ground. An itemized model has demonstrated for a

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variety of vehicles speed and operation condition using two [3] K. Kobayashi, K.C. CheoP and K. Watanabe, “Estimation of Absolute sensing techniques for bounce and pitch acceleration using Vehicle Speed Using Fuzzy Logic Rule-Based Kalman Filter”, American Control Conference, June 1995 simplified inverse model for the shift in time between two set for the wheels that was further used for the speed of [4] L. Wu & B. Coifman, “Improved Vehicle Classification From Dual- vehicles and its calculation and the benefit of this developed Loop Detectors In Congested Traffic”, Transportation Research, September 2014. system is that it has a higher efficiency rate. [5] M. H M. Ariff, H. Zamzuri, N.R.N Idris and A.M A. Mustafa, ”Integrated Vehicle Dynamic Control Using Composite Nonlinear VII. FUTURE ENDORSEMENT Feedback Method For Independent Wheel Drive Electric Vehicle”, Vehicle System Engineering I-Kohza, Malaysia-Japan International Institute Of Technology, Malaysia, 2011 As in the designed model, after successful [6] B. Coifman and S. Bum Kim, “Speed Estimation and Length Based implementation on simulation; it is suggested for the future Vehicle Classification from Freeway Single Loop Detectors”, that this project will be applied for different actions per Transportation Research, August 2009. result, also for the calculations for the speed of trains at various speed ranges with minimal errors. Moreover, the [7] J. Lan, J. Li, G. Hu, B. Ran, and L. Wang, “Vehicle speed measurement based on gray constraint optical flow algorithm”, International Journal For design could also be implemented on hardware for efficient Light And Electron Optics, January 2014 speed calculations for various ranges of speed although it would require a huge cost. [8] M. Mirzapour, T. X. Mei and I. Hussain, “Assessment of Fault Tolerance for Actively Controlled Railway Wheel set”, Proceedings of The UKAC International Conference On Control, September2012 REFERENCES [9] T.N Schoepflin & D.J. Dailey, “Dynamic Camera Calibration of Roadside Traffic Management Cameras For Vehicle Speed Estimation”, [1] T. X. Mei and H. Li, “Measurement of Absolute Vehicle Speed with a IEEE Transactions On Intelligent Transportation Systems, June 2003 Simplified Inverse Model”, IEEE Transactions on Vehicular Technology, March 2010 [10] M. Zhu, H. Chen, G. Xiong, “A Model Predictive Speed Tracking Control Approach for Autonomous Ground Vehicles”, Mechanical Systems [2] R. Wang and J. Wang, “Fault Tolerant Control with Active Fault and Signal Processing, March 2016 Diagnosis for Four Wheel Independently Driven Electric Ground Vehicles”, IEEE Transactions on Vehicular Technology, October 2011

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(c University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Analysis of Device-to-Device Communication System in the Presence of Multiple Co-Channel Interference

Zakir Hussain, Asim ur Rehman Khan, Haider Mehdi, Syed Muhammad Atif Saleem, Muhammad Asad Khan

National University of Computer and Emerging Sciences (NUCES), Pakistan [email protected], [email protected], [email protected], [email protected]

Abstract: In this paper, analysis of outage, channel capacity and symbol error rate (SER) analysis of Device-to- Device (D2D) communication systems is presented. The received signals of D2D communication system are affected by multiple co-channel interferers. The channel gain powers are considered to follow Gamma distribution. An expression for probability density function (PDF) expression of the signal-to-interference ratio (SIR) is presented. Based on expression of the PDF, the expressions for the outage, channel capacity and symbol error rate (SER) are presented. The performance of D2D communication system is then numerically analyzed and discussed under various conditions of channel fading and interference.

Keywords: D2D communication; co-channel interference; signal-to-interference ratio;

D2D communication on the cellular communication I. INTRODUCTION performance is studied in [9] with the help of a frame work With technological progression, the number of active proposed by the authors. Authors in [10], studied the cellular devices has increased rapidly. Due to bandwidth resource allocation and interference management for the hungry applications, the amount of data rate is excessive. D2D heterogeneous networks. This excessive data rate is mainly due to file sharing, video As the frequency band is limited and many wireless streaming, social networking, and so on. This trend is devices try to communicate simultaneously, in the absence of expected to increase exponentially in the next decade. The proper coordination between these devices, co-channel current cellular communication system is incapable to satisfy interference (CCI) occurs. Therefore, it is necessary to the increasing demand of high data rates. Device-to-Device consider CCI when analyzing performance of such wireless (D2D) communication system is one of the solutions to this systems. A D2D communication system is no exception [11, problem. The D2D communication is a promising 12]. Outage performance, channel capacity and symbol error rate (SER) are important metrics to analyze the performance technology which allows direct communication among close of a communication system [13], [14], [15]. Outage analysis proximity users without involvement of the base station for the D2D system in finite cellular networks has been (BS). In the published work, various D2D communication presented by authors in [13] with Nakagami fading and scenarios are discussed. In [1], authors have proposed a Rayleigh distributed multiple interferers. In [11], authors network assisted signaling algorithm for Device-to-Device have studied the outage performance of D2D systems over (D2D) devices discovery. Authors have proposed a time- Weibull-lognormal and Weibull-gamma channels with varying graph model to characterize the impacts of both gamma shadowed Nakagami co-channel interference. The social selfishness and individual on the D2D channel capacity is analyzed by authors in [14] over Rician communications in [2]. In [3], authors study the D2D fading channel affected by multiple Rician faded CCI. In communications over  and  fading channels. A [15], authors have analyzed channel capacity by considering framework based on the stochastic geometry to assess the Rician channel and Rayleigh distributed interferers. In [16], coverage probability for cellular as well as D2D networks is SER performance of M-PSK is studied for the Rayleigh and presented in [4]. In [5], authors study the joint resource block Rician channels. assignment and transmit power allocation issues, for the In this work, different from the published work, our optimization of the network performance. To maximize the objective is to analyze outage, channel capacity and SER performances of D2D systems with multiple co-channel D2D offloading utility an optimal content pushing technique interferers. Channel fading conditions are also considered for based on the user interests and sharing is proposed in [6]. In the desired D2D communication signals and the interferers. [7], various resource allocation techniques for the full-duplex The effects of the path-loss are also considered. The wireless D2D communication are discussed. A probabilistic distance channel gain powers are assumed to be Gamma distributed. and path-loss model is presented in [8] to analyze the Gamma distribution is considered here because it is a performance of D2D communication systems. Effects of mathematically tractable generalized distribution. It also University of Sindh Journal of Information and Communication Technology (USJICT) , Vol.2(1), pg:61-67

x models severely faded channel conditions [17]. The  ex k 1 expressions of outage, channel capacity and SER metrics, fxxk() 0, > 0, > 0.,  (1) based on the probability density function (PDF) expression  k ()k of the signal-to-interference ratio (SIR) are presented. The rest of the paper is presented as follows. In Section II, expressions of outage, channel capacity and SER are In (1), shape parameter is k, scale parameter is θ and gamma presented. Based on these expressions, numerical results are function is given by  . [23]. The fading power of the presented in Section III. Finally, this paper is concluded in Gamma variable is related to the scale parameter θ whereas Section IV. the severity of fading is determined by the shape parameter k [21]. In this paper, a simplified version of the path-loss II. SYSTEM MODEL model is also considered to include the effects of path-loss A scenario is considered in which a pair of D2D on the overall performance of D2D communication [22]. communication devices is communicating in interference Power of the received desired signal is given as limited environment [18]. In Fig. 1, the system layout is shown. Multiple co-channel interferers in the system are 2 a c assumed to be equidistant from the D2D receiver, and  0 (2) SPd  1 independent and identically distributed (i.i.d) [19]. A flat  4c0 c fading channel is assumed. The channel gain powers are considered to be Gamma distributed for the D2D pair and co-channel interferers. where Sd is the power of the D2D signal, P1 is the transmitted signal power, c is the distance between the D2D pair, a is path-loss exponent 25a  ,  is wavelength and

c0 is the reference distance (1 to 100 meters). Similarly, the i-th interferer power is

2 d b I1  0 (3) IP = 2  4 d d d 0  I2 c d where I is the power of the i-th interferer, P2 is the transmitted power of i-th interferer, distance between an i-th d interference source and D2D receiver is d, b is path-loss IN exponent and d0 is the reference distance. Based on (2) and (3), D2D system signal-to-interference ratio (SIR) i.e.  , is

a 2a hPc 2 c0   ,  Nbb P d 2 (4) Figure 1. Layout of a D2D communication system in the presence of  1 d0  multiple CCI  i i1

Device-to-Device pair where h and αi are independent and Gamma distributed N Interferers (IN) channel-gain powers of D2D signal and the i-th interferer, Desired communication signal between D2D Pair respectively, and number of co-channel interferers is N. c is D2D pair distance The SIR PDF expression of our system, i.e., f r, will now Interference signal from an interferer at distance d be presented with the help of the formula  The distance between an interferer and the D2D f r u f ru f u du [24] as     SI    receiver is d 0

PDF of the Gamma distribution is as follows [20]

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mm() r 1 TTwhere M is the order of the modulation. In (9),  . is frr ()   (5) Bm(,) confluent hypergeometric function of second kind [23]. T 

In (5), δ and m are the shape parameters of D2D and III. NUMERICAL RESULTS interference signals, respectively, B(.) is the beta function Numerical results based on our mathematical [23], ρ and σ are the scale parameters of the desired and the expressions of Section II are discussed in this section. interference signals, respectively, and mT N m . Based on Expressions are valid for arbitrary values of channel and (5), the cumulative distribution function (CDF) is interference parameters. The reference distance for D2D pair c0 and co-channel interferer d0 are assumed to be 1 1()m T meter. The number of interferers is assumed to be 5. For the

Frrr () = 1  outage performance analysis, the SIR threshold is set to be  (6) 10 dBm. In Fig. 2, outage of D2D system is shown. For D2D pair transmitted power P1 and path-loss exponent a are 1,1;1;21Fmr  T  considered to be 31.8 dBm and 3, respectively. The transmitted interference power P2, path-loss exponent b, In (6), F . is the hypergeometric function [23]. The 21  fading channel shape parameter m, and distance between the outage probability of a communication system is the co-channel interference and the receiving D2D device are probability that the SIR of a system drops down a predefined considered to be 27 dBm, 2.8, 2 and 40 meters, respectively. R threshold R, i.e. [17]. Hence, D2D The distance between D2D pair, i.e., c and the shape Pout f r dr =     0 parameter δ of the desired signal are varied. From the figure, communication system outage probability is [23] it can be observed that the outage performance of the system for the higher values of δ is better than that of the lower

1 () mT values. The reason is that as the value of δ is increased, the  fading conditions of the channel improves which results in RR1  improved outage performance of the system. It is also P   out  Bm( ,) observed that as the distance between the D2D pair T (7) increases, i.e., the value of c is increased, the outage  performance degrades. It is due to the reason that the 1,1;1;21FmRT   transmitted signal strength decreases with distance due to path-loss effects, i.e., the inverse relation between received Now, by using the channel capacity expression power and the distance. Outage with varying c and path-loss  exponent of the desired D2D signal is shown in Fig. 3. The Crfrdrlog1()() [25], after some algebraic  2  values of P1, δ, P2, m and b are fixed at 31.8 dBm, 2, 27 0 manipulations the capacity expression is [23] dBm, 4 and 3, respectively. From the figure, it can be observed that outage probability is low for lower values of a and high for higher values of a at same values of c. It is  1 1,1,1 G2,3  because as the value of a increases, loss in desired signal 3,3 m ,1,0 strength increases with the increase in path-loss severity. T (8) C  Hence, outage of D2D system degrades. ln(2),))Bmm((TT

wx, where Gyz, is the Meijer-G function [23]. Next, SER expression of the D2D communication system is shown by considering M-ary phase-shift keying (M-PSK) [26]. SER of M-PSK scheme is given as [23] and [26]

2   (M  1) 1 M sin  M  ( )   ;1md ;    T (sin )2 0   PMPSK  (9) Bm(,)T

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increases, system suffers from degraded SIR performance, resulting in deteriorated outage performance.

Figure 2. Outage performance with varying desired signal shape parameter Figure 3. Outage performance with various path-loss exponents of the desired signal In Fig. 4, outage analysis with varying path-loss exponent of the interfering signals b and the distance c is shown. The values for P1, δ, P2, m and a are considered to be 31.8 dBm, 2, 27 dBm, 4 and 3, respectively. The distance d is considered to be 25 meters. From the figure, it is observed that outage performance is better for higher values of b as compared to lower values. Because as the value of path-loss exponent is increased, the power level of the interfering signal reduces because of path-loss effects. Therefore, overall SIR of the system increases resulting in an improved outage performance of the system. In Fig. 5, outage performance is shown with varying path-loss exponents of the desired and the interfering signals. The path-loss exponent values a and b are considered to be equal. The values of the parameters P1, P2, δ, m and d are fixed at 31.8 dBm, 27 dBm, 2, 2, 35 meters, respectively. It is evident from the figure that the performance of the system Figure 4. Outage performance with various path-loss exponents of the is better for higher values of a and b for c < d where d = 35 interference meters. It is because the transmitted powers of D2D and interferer are affected by similar channel conditions, therefore, when c < d where d = 35 meters, desired signal source being nearer to the receiver than interferers causes overall improved SIR conditions and better outage performance for higher path-loss exponent values. However, for c > d where d = 35 meters, the outage performance is better for the lower values of a and b. It is because the system with the desired signal under less severe path-loss conditions show better SIR conditions. In Fig. 6, outage performance with varying number of co-channel interferes, i.e., N is shown. The values for P2, δ, m, a, b, c and d are considered to be 27 dBm, 3, 2, 3, 2.7, 26 meters and 45 meters, respectively. It is clear from the figure that the outage worsens when the number of interferers are increased. It is because as the number of interferers

Figure 5. Outage performance with equal path-loss exponents of the desired and interference signals

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Figure 6. Outage performance with various co-channel interferers Figure 8. Channel Capacity (bits/s/Hz) with various path-loss exponents of Channel capacity performance of the system with varying the interference values of m and d is shown in Fig.7. Values of P1, δ, c, and P2 are fixed at 34 dBm, 3, 40 meters and 26 dBm, In Fig. 9, channel capacity performances are shown with respectively. From the figure, it can be seen that capacity varying values of the path-loss exponents a and b. The path- performance is better for small values of m, i.e., under severe loss exponent values are considered to be equal. Values for interference fading conditions. However, for the higher P1, P2, δ, d and m are 34 dBm, 27 dBm, 3, 35 meters and 2, values of m, i.e., under better fading conditions, the channel respectively. The figure clearly shows that capacity of D2D capacity is almost insensitive to the variations in the system is better for the higher values of path-loss exponents interference fading conditions. In Fig. 8, channel capacity when c < d where d = 35 meters. However, when c > d performances are shown with varying path-loss exponent of where d = 35 meters, the capacity is better for the lower the interfering signal and d. The values of P1, δ, P2, m and a values of a and b. SER performance of 8-PSK system with are considered to be 34 dBm, 2, 27 dBm, 5 and 2.8, various values of the interference shape parameters is shown respectively. As shown in the figure, the channel capacity in Fig. 10. The values of P1, P2, δ, a and b are fixed at 34 performance is improved for the higher values of b and d. dBm, 24.77 dBm, 5, 2.5 and 3, respectively. From the figure, The reason is that for the higher values of b and d it can be observed that as the values of m are increased, SER interference signals strengths deteriorate. improves. However, for higher values of m, i.e., better interference fading conditions, the system SER performance is insensitive to change of interference fading conditions. Also, when the distance between the D2D pair increases, i.e., c, the SER performances converge for all interference fading conditions. It is because when the distance between D2D pair increases, path-loss becomes a dominant factor affecting the SER performance of the system. SER performance analysis of 8-PSK modulated system with varying N and P1/ P2 is shown in Fig.11. The values for P2, δ, m, a, b, c and d are assumed to be 24.77 dBm, 5, 2, 2.7, 3.5, 30 meters and 70 meters, respectively. From the figure, it is observed that SER performance of the system deteriorates as the number of co-channel interferers are increased. Moreover, it is observed from the figure that SER of the system improves with increase in power P1.

Figure 7. Channel Capacity (bits/s/Hz) with various interference shape parameter values

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IV. CONCLUSIONS In this work, outage, channel capacity and SER performances of D2D communication systems is studied and analyzed with multiple co-channel interference. Path-loss effects are also considered. The power of the channel gain is assumed to be Gamma distributed for both desired and interference signals. Based on the PDF of the SIR, the expressions for the outage performance, channel capacity and SER are presented. It is observed that various D2D communication system factors, like, fading and path-loss, affects the overall outage, channel capacity and SER performances. It is also observed that co-channel interference despite being affected by various hostile conditions, like, fading and path-loss, degrades the performance of D2D communication system.

REFERENCES Figure 9. Channel Capacity (bits/s/Hz) with equal path-loss exponents [1] M. Naslcheraghi, L. Marandi, S. A. Ghorashi, “A novel device- to-device discovery scheme for underlay cellular networks,” 2017 Iranian Conference on Electrical Engineering (ICEE), Tehran, May 2017, pp. 2106-2110. [2] Gao C, Zhang H, Chen X, Li Y, Jin D, Chen S., "Impact of Selfishness in Device-to-Device Communication Underlaying Cellular Networks,” IEEE Trans. Veh. Technol., vol. 66, no. 3, pp. 9338–9349, May 2017. [3] Y. J. Chun, S. L. Cotton, H. S. Dhillon, A. Ghrayeb, M. O. Hasna, “A Stochastic Geometric Analysis of Device-to-Device Communications Operating Over Generalized Fading Channels,” IEEE Trans. on Wireless Commun., vol. 16, pp. 4151-4165, July 2017. [4] A. Al-Rimawi, D. Dardari, “Analytical Characterization of Device-to-Device and Cellular Networks Coexistence,” IEEE Trans. on Wireless Commun., vol. 16, pp. 5537-5548, June 2017. [5] T. Yang, R. Zhang, X. Cheng, L. Yang, “Graph Coloring Based Resource Sharing (GCRS) Scheme for D2D Communications Underlaying Full-Duplex Cellular Networks,” IEEE Trans. on Veh. Technol., vol. 66, pp. 7506-7517, Aug. 2017. [6] Y. Pan, C. Pan, H. Zhu, Q. Z. Ahmed, M. Chen, J. Wang, “Content offloading via D2D communications based on user interests and sharing willingness,” IEEE Int. Conf. on Commun. Figure 10. 8-PSK SER with varying interference shape parameters (ICC), Paris, May 2017, pp. 1-6. [7] A. N. Kadhim, F. Hajiaghajani, M. Rasti, “On selecting duplex- mode and resource allocation strategy in full duplex D2D communication,” Iranian Conf. on Elect. Eng. (ICEE), Tehran, May 2017, pp. 1640-1645. [8] F. Tong, Y. Wan, L. Zheng, J. Pan, L. Cai, “A Probabilistic Distance-Based Modeling and Analysis for Cellular Networks With Underlaying Device-to-Device Communications,” IEEE Trans. on Wireless Commun., vol. 16, pp. 451-463, November 2017. [9] H. ElSawy, E. Hossain, M. S. Alouini, “Analytical Modeling of Mode Selection and Power Control for Underlay D2D Communication in Cellular Networks,” IEEE Trans. on Commun., vol. 62, pp. 4147-4161, October 2014. [10] A. Celik, R. M. Radaydeh, F. S. Al-Qahtani, M. S. Alouini, “Joint interference management and resource allocation for device-to-device (D2D) communications underlying downlink/uplink decoupled (DUDe) heterogeneous networks,” IEEE Int. Conf. on Commun. (ICC), Paris, May 2017, pp. 1-6. [11] M. V. Banđur, Đ. V. Banđur, B. M. Popović, “Outage probability analysis in shadowed fading channel with multiple cochannel interferences,” 21st Telecommun. Forum Telfor Figure 11. 8-PSK SER with various co-channel interferers (TELFOR), Belgrade, Nov. 2013, pp. 299-302. [12] J. A. Anastasov, G. T. Djordjevic, M. C. Stefanovic, “Outage

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probability of interference-limited system over Weibull-gamma fading channel,” IEEE Electron Lett., vol. 48, pp. 408-410, April 2012. [13] J. Guo, S. Durrani, X. Zhou, H. Yanikomeroglu, “Device-to- Device Communication Underlaying a Finite Cellular Network Region," IEEE Trans. on Wireless Commun., vol. 16, pp. 332- 347, Jan. 2017. [14] X. Jian, X. Zeng, A. Yu, C. Ye, J. Yang, “Finite series representation of Rician shadowed channel with integral fading parameter and the associated exact performance analysis,” China Commun., vol. 12, pp. 62-70, Mar. 2015. [15] C. Liu, B. Natarajan, “Power-Aware Maximization of Ergodic Capacity in D2D Underlay Networks,” IEEE Trans. on Veh. Technol., vol. 66, pp. 2727-2739, March 2017. [16] Yang J, Zhu C., "Computing the Average Symbol Error Probability of MPSK System with Receiver Imperfections," 8th Int. Conf. on Wireless Commun., Networking and Mobile Computing (WiCOM), Sept. 2012, pp. 1-4. [17] Shankar, P. Mohana, “Statistical models for fading and shadowed fading channels in wireless systems: A pedagogical perspective,” Wireless Personal Commun., vol. 60, pp. 191-213, March 2011. [18] K. S. Hassan, E. M. Maher, “Device-to-Device Communication Distance Analysis in Interference Limited Cellular Networks,” ISWCS 2013; The 10th Int. Symp. on Wireless Commun. Syst., Ilmenau, Germany, pp. 1-5, Aug. 2013. [19] N. Bhargav, C. R. N. da Silva, Y. J. Chun, S. L. Cotton, M. D. Yacoub, “Co-Channel Interference and Background Noise in  Fading Channels,” IEEE Commun. Lett., 21, pp. 1215- 1218, May 2017. [20] N. L. Johnson, S. Kotz, N. Balakrishnam, “Continuous Univariate Distributions”, 2ed; vol. 1, Wiley: New York. [21] A. Maaref, R. Annavajjala, “The Gamma Variate with Random Shape Parameter and Some Applications,” IEEE Commun. Lett, vol. 14, pp. 1146-1148, December 2010. [22] Andrea Goldsmith, “Wireless Communications”, Cambridge University Press: Cambridge, England, 2005. [23] I. S. Gradshteyn, I. M. Ryzhik, “Table of Integrals, Series, and Products", 7th ed.; Academic: San Diego, CA, USA, 2007. [24] I. Trigui, A. Laourine, S. Affes, A. Stephenne, “Outage Analysis of Wireless Systems over Composite Fading/Shadowing Channels with Co-Channel Interference,” IEEE Wireless Commun. and Networking Conf., Budapest, April 2009, pp. 1-6. [25] I. Trigui, A. Laourine, S. Affes, A. Stephenne, "Performance analysis of mobile radio systems over composite fading/shadowing channels with co-located interference," IEEE Trans. on Wireless Commun., vol. 8, pp. 3448-3453, July 2009. [26] B. Barua, M. Abolhasan, D. R. Franklin, F. Safaei, “SEP of Multihop Relay Networks in Nakagami-m Fading Channels,” IEEE 78th Veh. Technol. Conf. (VTC Fall), Las Vegas, NV, pp. 1-5, Sept. 2013.

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ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Development of an Arduino Based Device for Early Detection of Gas Leakage in Hospitals & Industries

Sehreen Moorat, Hiba Pervaiz, Faryal Soomro, Maham Mahnoor

IBET, Liaqat University of Medical and Health Sciences, Jamshoro E-mail [email protected]

Abstract: Leakage of gases in a system makes infrastructures and users vulnerable, it can occur due to its environmental conditions or old groundwork. Hospitals and industries where many types of gases are used like nitrogen, mono oxide, due to small amount of concentration in air can cause toxicity whereas detection of small amount of gas at its initial stage is very difficult task. Many systems have been developed which were failed in past to give accuracy in its implementation. In this research a portable detection system for the small leakage of gases has been developed, gas sensor (MQ-2) is used to find leakage when it is at its initial phase. The sensor and transmitting module senses the change in level of gas by using a sensing circuit. When a concentration of gas reach at a specified threshold level it will activate an alarm and sends a text massage to receiving module. The proposed system works well in hospitals, home, and industries.

Keywords: Gases; Detection; Arduino; MQ-2; Alarm

I. INTRODUCTION All manuscripts must be in English. University of Sindh Journal of Information and Communication Technology (USJICT) strictly follows the manuscript preparation guidelines as provided by the IEEE. These guidelines include complete descriptions of the fonts, spacing, and related information for producing your journal article.

In the 19th century, there was greater usage of natural gases than ever before which mainly consist of methane which was also harmful for working conditions if exceeded a limit. Exposure or inhalation to gas can cause many diseases like pulmonary edema, bronchiolitis, pneumoconiosis which can also lead to cancer if they are not treated at initial stages [1]. Detection of harmful gas leakage is usually done by detectors along with an audible alarm for alerting team if any injurious gas comes in contact with the sensor. So far, many sensors are used for detection like ultrasonic sensors, Figure 1. summary of gas leakage detection system infrared point sensors, semiconductor sensors and electrochemical sensors [2]. These all sensors are used at II. RELATED WORK different places like hospitals, industries, homes, vehicles etc. number of systems are developed for the detection of In early industrial ages workers were used to carry canary gas at various stages which shown different results with birds with them in mines in order to sense harmful gases; respect to accuracy. This research represents a project which kyle Schmidt [4]. According to health administration carbon is based on Arduino and gas detection sensor for the dioxide gas which is odorless and colorless gas, is a recognition of small amount of gas this project’s competence poisonous gas which can cause many deaths. Canaries which is not only limited to its low cost, also its structure is are in cages along with the worker warn them by making practical and can be installed easily in factories, industries sound when they detect presence of carbon dioxide. and also in hospitals which are in danger of exposure of gas According to BBC report which was published in 1986, [3]. This automatic system of recognizing gases not only along with canary birds’ mice were also use to detect gases save lives but also saves time due to its simple installation in mines but accurate and reliable result was got by using system. canaries due to their instant respond to physical response and University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:68-72 sensitivity to deadly gases. Richard E. Soltis et all in their research stated that, in 21 century canaries were replaced by electronic gas indicators which were more reliable in detecting poisonous gases.

EHS in their report showed that valve was used in first gas monitor system which was capable enough to measure methane gas in the atmosphere was manual whereas nowadays all detectors work automatically [5]. There are also some detectors present which are used to monitor the presence of various gases at time and record them in the database; like gas-ranger it is capable to identify level of hydrogen, methane, sulfide and oxygen in air.

Luay et all worked on a sensing circuit which was designed to detect variation in gas concentration within house and sends an alarm to authorize person. Semiconductors are also widely used for the detection of dangerous gases like carbon monoxide and methane which are the cause of explosions or toxic accidents at different places, Emil et all in their research present a detection method along with its characteristics which were helpful for the future designing [6]. Parsanth Krishnan and D.Jackson in their successful experiment, found the presence of CH4, H2s, CO and O2 concentration level in atmosphere along with the temperature effect in order to check cross sensitivity between Figure 2. Flow chart of detailed methodology sensors. Numerous software simulations are also used in order to find the absorption of gas Zhou Bin et all shown that on the basis of phase lock algorithm and wavelength A. Hardware: Brief Preliminaries modulation harmonic curve and concentration of targeted gas 1) MQ- Gas Sensor are obtainable [7]. Sahil Adsui et all in their research stated a The irresistible specialty of generating analog output in method for detecting different parameters like pressure, MQ gas sensors is remarkably effortless. Exposure of sensor sound, temperature and humidity, based on these thing they is likely to interact with environmental gases and start used a microcontroller along with a sensor to make system analyzing it. Further the process generates voltages which is easy to use and potable [8]. conveyed as current to the output pins. The sensing element Comparatively our proposed system shows promising is based inside superficial structure of sensor. Current is results than previous work in terms of portability, it’s directly related to the sensing section. Once the current specificity and results along with new methods of sending reaches the sensing element it is then ionized and thus known message to the three assigned numbers in an emergency as heating “heating current”. Hence it is directly conditions, which is not being used previously by any proportional to the resistance, it alters the values accordingly researcher. Earlier, microcontroller and semiconductors are in the order of resistance and current relationship. It has the used for the configuration but we have used Arduino which ability to fluctuate the values as required in accordance with is easy to understand and operate by anyone. the precision of gas detection regulated by the potentiometer resides inside it [9]. III. METHODOLOGY: The methodology is divided into three main steps. The first step is to detect the leakage of gas by using MQ-2 sensor. Secondly after the detection of presence of gas MQ-2 sensor, it gives signal to Arduino software which in last was used to send signal to GSM module and alarm. The detailed methodology is shown in flow chart given below;

Figure 3. MQ-2 Sensor P.C. Google

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1) Features 4) Threshold value Threshold value has been set according to the TABLE I. Features of MQ-2 environmental occupational safety. Detection of various Sensitive for Butane, Methane and gases like methane and nitrogen has been checked where green light showed the normal state with the no alarming LPG sound whereas Red light showed above threshold values that Operating voltage 2.5V-5.0V are customary according to the environment and distance from sensor. The normal ranges checked were from 100 Dimensions 40.0mm*21.0mm PPM to 10000 PPM.

Fixing Hole size 2.0mm IV. RESULTS AND INTERPRETATION In this project there are two phases of results. Normal 2) Arduino Hardware state and Harmful/ Emergency state, analyzed results and Arduino is easy to use and open source platform for observations are as follows, beginners especially who have no previous experience of high level electronics. Many interactive objects which can respond by signals can be developed by this open plat form. Due to its special features and high performance it is a perfect microcontroller for this project. Arduino consists 14 input or output pins, 16 Mega Hertz crystal oscillator along with 6 analog inputs, a power jack, reset button and a USB connector. Arduino microcontroller can be programmed with the help of integrated development environment (IDE). For loading a new program code, it does not need any separate hardware just it requires only connection with USB cable [10].

Figure 6. complete circuit

A. Normal state After compilation and uploading of program, it is firstly calibrated. Secondly the green LED is turned on, indicating normal state when there is no indication of dangerous gases and all gases lie within their normal range

Figure 4. Arduino hardware

3) GSM MODULE The circuit of arduino is interfaced with arduino based GSM module (SIM mini 900) for the remote alarming purpose. It facilitates the allocated personnel to get alert at remote via text message in order to cope up with the danger immediately.

Figure 7. Green LED at Normal State

And the values are shown in PPM on the serial monitor of Arduino.

Figure 5. GSM module

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C. Output of GSM At harmful state, message will be delivered to the assigned numbers (at least three numbers) which are added to the module for sending alert through GSM module. The message sent by the Arduino can be modified depending on the operator/the one who is using it, as we have for testing purpose written the message “BAD PPM” and was delivered as it was written.

Figure 8. PPM values in Serial Monitor at Normal State

B. Harmful State Whereas during harmful state the overall process remains same but when the particles per minute (PPM) exceeds the threshold value the green LED shifts to red LED indicating harmful state.

Figure 11. SMS Delivered and Received

V. CONCLUSION Purpose of this paper is to make a low cost detection

Figure 9. PPM Value Higher than Threshold in Serial system of gas which helps to reduce the incidents. Aim of Monitor our research was to reduce the ratio of unexpected deaths due to leakage of hazardous gases at work place. The gas leakage When the sensor is exposed to gas more than threshold, monitoring system by using MQ-2 sensor and Arduino was red LED is turned on. successfully developed and implemented. There are many projects which are made for the detection of gases but this project consists many different features like due to its light weight and portability, it can be carried and easily installed anywhere. Our proposed system shown the promising results. Hence, we would like to expand our proposed prototype in future by adding temperature sensor as well.

With the advancement in technology, such early gas detection sensors could be easily approachable to users in everyday life, where life comes into contact with gases either directly or indirectly. Not only hospitals but homes, industries, cars with gas kits, fueling stations etc. can implement this device and receive long lasting boons. This device with more accurate precision and long range of sensation will make its place in higher industrial areas. Figure 10. Red LED Indicating Danger

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VI. ACKNOWLEDGMENT Journal of Engineering Research and Applications Praise to Almighty Allah, the most gracious and the most (IJERA) ISSN: 2248- merciful. Without Allah’s guidance our accomplishment would have never been possible. We would like to express our gratitude to those who have helped us in the course of this paper. First and foremost, we would like to thank our teachers without their guidance it would never be possible.

VII. REFERENCES [1]. Shimizu, Y.; Egashira, M. “Basic aspects and challenges

of semiconductor gas sensors.” MRS Bull. 1999.

[2]. Batzias, F., Siontorou, C., Spanidis, P.-M., “Designing a reliable leak bio-detection system for natural gas pipelines.” Journal of Hazardous Materials 186 (1), 2011. [3]. Brodetsky, I., Savic, M.,“Leak monitoring system for gas pipelines. In: Acoustics, Speech, and Signal Processing,” ICASSP-93. IEEE International Conference on. Vol. 3, 2004. [4]. Salma, Aya Bani, et al. “Methane and Carbon Monoxide

Gas Detection System Based on Semiconductor Sensor -

IEEE Conference Publication.” Methane and Carbon Monoxide Gas Detection System Based on Semiconductor Sensor - IEEE Conference Publication, IEEE, 23 Feb. 2005. [5]. Emil Cordos, et al. “Methane and Carbon Monoxide Gas Detection System Based on Semiconductor Sensor.” Methane and Carbon Monoxide Gas Detection System Based on Semiconductor Sensor, IEEE, 11 Dec. 2006. [6]. Prasasnth Krishnan, and David Jackson.

“Multifunctional Gas Detection Based Temperature

Compensation and Data Fusion.” Multifunctional Gas Detection Based Temperature Compensation and Data Fusion - IEEE Conference Publication, IEEE, 23 May 2016. [7]. Zhou Bin, et al. “The Simulation and Analysis of Gas Detection System in Infrared Absorption Spectrum Based on LabVIEW.” The Simulation and Analysis of Gas Detection System in Infrared Absorption Spectrum Based on LabVIEW - IEEE Conference Publication, IEEE, 7

Jan. 2016.

[8]. Sahil Adsul, et al. “Development of Leakage Detection System.” Development of Leakage Detection System - IEEE Conference Publication, IEEE, 5 Aug. 2017,

[9]. Sulzer, Philipp; Hartungen, Eugen; “Fire Protection Guide to Hazardous Materials”, 13th edition, National Fire Protection Association (NFPA) One Battery Park, Quincy, MA 0226, 2002.

[10]. Shinde S.,Patil S.B, Patil A.J. “Development of Movable Gas Tanker Leakage Detection Using Wireless Sensor Network Based on Embedded System”. International

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(c University of Sindh Journal of Information and Communication Technology (USJICT) Volume 2, Issue 1, January 2018

ISSN-E: 2523-1235, ISSN-P: 2521-5582 © Published by University of Sindh, Jamshoro Website: http://sujo.usindh.edu.pk/index.php/USJICT/

Offline Signature Recognition and Verification System Using Artificial Neural Network

Aqeel-ur-Rehman1, Sadiq Ur Rehman2, Zahid Hussain Babar1, M. Kashif Qadeer1 and Faraz Ali Seelro1

1Department of Computing, Hamdard University Karachi, Pakistan 2Department of Electrical Engineering, Hamdard University Karachi, Pakistan [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract: There are several alternative life science techniques that are used to identify human, these techniques are namely eye recognition, face recognition, finger print recognition and currently a well-known signatures recognition and verification. The utilization of signatures is in all legal and financial documents. Verification of signatures now becomes necessary to distinguish between original and forged signature. A computer based technique is necessitated in this regard. Verification of signatures can be performed either offline or online. Under offline systems, signatures are taken as an image and recognition is performed using some artificial intelligence techniques including neural networks. We have worked on off-line Signature Recognition and Verification System (SRVS) by taking artificial neural network technique into account. Signatures are taken as image and after some necessary pre-processing (i.e. to isolate the signature area) training of system is done with some initial stored samples to which authentication is needed. MATLAB has been used to design the system. The system is tested for several scanned signatures and the results are found satisfactory (about approximately 95% success rate). Image quality plays an important role as poor quality of signature image may lead to the failure to recognize/verify a signature. Increase in the attributes/ features of signature will increase the verification ability of the system but it may lead to higher computational complexity

Keywords: Security, Authentication, Off-line, Signature Verification, Image Preprocessing, and Neural Network

the dynamic features are time dependent. Dynamic features I. INTRODUCTION are extracted using electronic tablet or PDA. Handwritten signature of the person is recorded in Remaining part of this paper is divided into five parts. different patterns for authentication and authorization Literature review has been done in section two. Section three purpose which can be used for bill of exchange, any contains the proposed system that is going to be used for document or any security countersign. There are several offline signature recognition and verification. System alternative life science techniques that are used to identify implementation is in section four and finally conclusion and human, these techniques are namely eye recognition, face future directions can be found in section five. recognition, figure print recognition and currently a well- known signatures recognition and verification. This II. LITRATURE REVIEW technique is extremely inspired authentication technique for With the use of modern technologies, there are several researchers to push secure and ideal technique within the ways for human identification. Hand written signature is one world. Signatures recognition and verification systems are of them. It is important to perform verification process on divided into two categories which are namely offline hand written signature in order to distinguish between signature recognition and verification system and online original signature and the forged one. Reference [1] can be signature recognition and verification system. seen to find details regarding two types of precision errors In offline signature system, format of stored signature is occur in signature verification which are namely “False in image format. Prior to the feature extraction, Rejection Rate” and “False Acceptance Rate”. Reference [2] preprocessing is mandatory to be performed on a scanned provides the comprehensive details for the types of forgery image to separate signature segment and to eliminate noise present in handwritten signature. In order to maximize the part if any. efficiency of signature verification system, there are number In on-line signature system, person’s signature is of methods for such verification. Some of the recent work extracted from capacitive tablet or PDA that provides x-y under the heading of offline signature verification can be coordinates, pressure reading etc. These raw data values are found as in [3] where the authors have suggested a grid- then used to calculate various features. Signature based template matching scheme. Based on pixels intensity, authentication can be obtained by two ways that are Static an offline verification model has been proposed in [4] to find and Dynamic. Static features are independent of time while the genuine signature. Lots of work have been done on SVM University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:73-80 for offline signature verification, a detailed review can be statistics of the training set, moreover depending on distance found as in reference [25], Further-more, as in references [5- measure decision has to be made. 7] different researchers have applied different concepts to achieve the efficient method for offline signature 4) Statistical Approach: Relationship, deviation etc. in verification. Some of the most commonly used techniques between data can be determined by using statistical for hand written signature verification are as follows: knowledge. If the correlation between set of data items is to A. Signature Verification Techniques be revealed, the concept of Correlation Coefficients is 1) Hidden Markov Model: For the purpose of analyzing usually used. To verify the recently introduced signature, sequence in signature verification, it is recommended to use statistical approach pursues the concepts of correlation to Hidden Markov Model (HMM). Signature by hand is determine the amount of dissimilarities among the newly considered as a string of vectors of values which is introduced signature and already stored signature. associated with all individual points of signature on its path. Kolmogrov Smirnov Statistic is a unique method for Thus, it is important to build an effective signature signature verification in which various features are verification system by selecting appropriate set of feature extracted. This feature includes gradient of image, statistical vectors for Hidden Markov Models. Models type is features (distribution of signature’s pixels, geometry and stochastic and these type models have the ability to soak up topographical descriptors). The classification consists of the similarities of pattern variability. In Hidden Markov collecting difference in the signatures of the writer and Models, matching of signatures and models take place. This acquiring distribution in distance space. If signature is to be matching can be achieved by either probability distribution verified, then procedure secures the distribution that is of signature’s feature or by finding the probability of the compared with the recognized one. Kolmogorov-Smirnov way calculation take place for the original signature. test is used to find the probability of resemblance. Signatures are considered as by original person when the resulting probability is greater than the test signatures 5) Support Vector Machine (SVMs): SVMs are basically probability, otherwise the signatures get rejected. HMM an algorithm, these machine learning algorithms requires system utilizes only global features and “Sinograph” which high dimensional feature space and calculate inequality is a discrete random transform. This discrete random among the classes of given data to generalize unseen data. transform is to be computed for all individual binary Signature’ features (global, directional & grid) are used by signature image that lie under the range of 0 – 360. the system and for the verification and classification system uses SVM as in [9]. 2) Neural Network: In pattern recognition, neural networks (NNs) are widely used as they are powerful and 6) Self-Organizing Map (SOM) and Multilayer easy to use. The easiest way to start is to select feature set Perceptron (MLP): It has been suggested by Paigwar like height, length etc. of the signature by taking multiple Shikha et al. [22]. Self-Organizing Map (kind of Artificial specimens from various signer. Learning correlation among Neural Network)) is basically used to solve bundle of tasks. signature and its class (“genuine” or “forgery”) will be the That is the reason SOM is used in problems of pattern second step for the NN, once the relationship is developed. recognition. The architecture SOM is comprised on single Network contains the sample signature owned by a specific layer. In contrast with SOM, MLP is multilayered (Input signer. Thus, to build the global aspects for the signatures layer, output layer and hidden layer). MLP is used to that have been done by hand, NNs are best suited. The recognize information even from noisy data. system that has been proposed in this paper will utilize 7) Back Propagation Neural Network: Signature signature contour structure features, altered directional verification and recognition can also be done by using Back feature and some other features for example length skew, Propagation of Neural network as proposed by Nilesh Y. area of surface, centroid etc. Choudhary and can be seen in [23]. In this, for extraction of features, methods like invariant central moment and Zernike 3) Template Matching: In template matching, for the moment are used. Easy implementation is the key advantage detection of skilled forgeries there are two proposed of Back Propagation method which is based on three-layer techniques as in. [8]. Optimal matching of projection architecture. profiles of signature patterns that are one dimensional is one method while other method is of two-dimensional signature 8) Signature Envelope and Adaptive Density patterns which are dependent on the elastic matching of the Partitioning Approach: This approach has been proposed by strokes. To authenticate given sample signature, there will Vahid Malekian et al. [24]. Partitioning takes place in this be an analyzation on variations of position by using method on a pre-processing image into four segments by making center of gravity as its center point. Each segment is

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:73-80 then further equally divided into four parts. So, in this way there are sixteen segments of signature in total. III. PROPOSED SYSTEM

Signatures are treated as a most promising authentication B. Image Preprocessing Techniques method in all legal and financial documents. So it is In this technique i.e., image pre-processing; verity of important to create such a technique which is efficient to range is present for the techniques that are used for verify (correct or forged) the handwritten signature. In the manipulation and modification of images. Initial step is banking industry, signatures for long have been used for image preprocessing for the process of signature verification automatic clearing of cheques. Signatures from human are and recognition which produces better results with huge generally considered as image which is identified by using accuracy rates. Table I is presenting the various important computer along with the techniques of neural network. In image preprocessing techniques used. order to solve this problem, offline-signature recognition and verification system by using artificial neural network is a TABLE I. IMAGE PREPROCESSING TECHNIQUES much better solution in our literature review analysis and in present research trend. The objective of this system is to verify signature by using average signature that has been obtained from the set of already stored signature thus reduce the time required for Signature verification. There are so many algorithms for which neural network can be implemented but having some advantages of back propagation algorithm. It is proven to be the first choice for neutral network implementation. It is easy to implement while keeping efficient neural network. If we discuss about backward propagation NN [10] of the structure, it consists of three layers (refer to Fig. 1) in which first is Input Layer second is Hidden layer and final Output layer. In middle layer named as Hidden layer work to propagate (On this layer nodes/samples are classified based on proposed techniques) information from one layer to other. Output layer basically holds the propagated data and make comparison of C. Feature Extraction that data which can be shown and results are shown using mentioned condition. After Image preprocessing, Feature Extraction is the essential step for signature recognition and verification. The objective of feature extraction is to create features that can be operated as comparison measurements. As it is noticed that the problems related to the signature verification is an extremely sensitive process, it is suggested to generate more than one feature measurement so that accuracy of the result can be enhanced. Table II is highlighting various important features that are utilized towards signature verification process.

TABLE II. IMAGE FEATURE EXTRACTION

Figure 1. Back Propagation Neural Network

A. Preprocessing and Features Selection for Proposed System SRVS After having detailed literature review as mentioned in Table I and II, the following pre-processing steps and features are finalized for signature verification

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1) Pre-processing Steps: • Image Resizing • Converting to Gray Scale Image • Background Elimination • Image Thinning • Bounding Box the Image

2) Image Features for Verification: • Signature area • Signature Ratio • Geometric Centre • Edge Points • Aspect Ratio

B. System Design System design is split into two main stages: 1) Training stage of Signature:This stage consists of following four major steps (implicitly 4 steps but explicitly 2 steps): • Retrieval of 10 signature images from a storage file • Image pre-processing

• Feature extraction • Neural network training Figure 2. System Training Steps 2) Testing stage of Signature: The testing stage is based on the following five important steps (implicitly 4 steps but explicitly 3 steps): • Retrieval of a 10 signature images from a storage file • Image pre-processing • Feature extraction • Checking output generated from a neural network • Application of extracted features to a trained neural network

Figure 3. System Testing Steps

C. Preprocessing on Signature Image Signature Image pre-processing is to manipulate and modify an image. It is counted as a first step in the process of

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signature verification and recognition and produce improved global properties of objects and to cut down the original results and higher accuracy rates, See table III image into a more compact representation [20]. An algorithm known as Stentiford algorithm is used for 1) Image Resizing: Image resizing is the first process of thinning process. preprocessing of signature image and it makes the image fit to box where that will be unique for all sizes. TABLE III. PREPROCESSING ON SIGNATURE IMAGE Let consider Height as H of inputted image and width as W Signature Image of the input image. We need to make homogenous image at

100*100 pixels by using equation as:

Xnew = (Xold * 100)/H; Original

Where Xnew is calculated using Xold (original X coordinate). Ynew = (Yold * 100)/W;

Where Ynew is calculated using Yold (original Y coordinate). Resized By using these equations, transformation of uniformed 100*100 pixels of input image can be achievable.

2) Converting to Gray Scale Image: Now a day, all the latest devices which are capable of image capturing and Gray Scaled scanning, use color. Due to this reason, we have used the color scanning device for the purpose of scanning signature images. Normally a color image is consisting of three color

matrices (labeled as RGB) and a coordinate matrix (x,y Background Eliminated \\\\ coordinate values of the image). Techniques given in this study are based on grey scale images, and for this reason, scanned or captured color images at first are converted to grey scale using equation as in [17] [20]. Thinned Gray color = 0.299*Red + 0.5876*Green +0.114*Blue

3) Background Elimination: In this step we focused on true object which is signature in this case, but when Boundary Bounded

signature is captured it has background which may be because of a page or another shadow. For removing this background from signature images, we used thresholding, used extensively for the image segmentation. In 5) Boundary Bounded Image: When images are thresholding, a value commonly known as threshold value captured we cannot have supposed to have exactly the same and is represented by “T” has to be chosen. Moreover, a dimensions which we have testing box. There is a huge value 0 is to be assign to those pixels having values smaller chance to have irregularities during image capturing and than or equal to threshold value “T”. Similarly, value 1 will scanning process which results in the fluctuation of be assigned to those who have values greater that “T” [20]. signature’s dimensions. Width to height of signatures By using threshold method, extraction of signature pixels proportion to signature even the one person can have same from its background pixels is possible. In this application, signature with different dimensions. Therefore, it is we are more interested in dark object with light background, important to remove the dimension fluctuation and attain a for this reason, careful selection of threshold value is benchmark for all input signature size. When process of required and applied to image pixel [17] as; normalization is going on, there will be no change in the characteristic ratio of signature’s height and width. There If f(x,y) ≥ T then will be an identical dimension for all the signatures. f(x,y) = Background else f(x,y) = Object Following is the equation used for normalization process:

4) Image Thinning: It is used with the purpose of getting Xnew = [(Xold-Xmin)/ (Xmax-Xmin)]*M rid from the thickness variation of pen by adjusting image one pixel thick. Thinning was introduced to represent the Ynew = [(Yold-Ymin)/ (Ymax-Ymin)]*M

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Where Xnew, Ynew = Normalized signature pixel 4) Edge Points: A point that has only one 8- neighbor is coordinates, Xold, Yold = Original signature pixel known as edge point. With the purpose to extract edge point coordinates, M = Normalized signature Width/height meant. in a particular signature, an element structure of 3×3 should D. Features Extraction have only one pixel equal to 1 and others equal to 0.

Feature extraction is a name of a process in which 5) Aspect Ratio: The ratio of width to height of the information is extracted from a raw data and that will work signature is said to be the aspect ratio which is represented in allocation stage. Data can be reduced within-class pattern variation and increases the inter-class variations. Thus, in by A. Coordinates of bonding box signature are determined order to achieve high performance in signature recognition and height Dy and the width Dx are measured using these system, selection of efficient feature extraction method is of coordinates. great importance. There are two characteristics of an efficient feature extraction algorithm namely: Invariance and reconstruct-ability. Features will have the capability to identify numerous varieties of signatures if they are invariant to definite signature transformations. Feature extraction is a second most important step for signature recognition and verification. The purpose of this step is to create features that IV. SRVS SYSTEM IMPLEMENTATION can be used as comparison measurements. As it is noticed To accomplish the results, we have implement offline that the problems related to the signature verification is an signature recognition and verification system using neural extremely sensitive process, it is suggested to generate more network toolbox on MATLAB and train no of samples than one feature/measurement so that accuracy of the result signatures of different users which are stored in file-storage can be enhanced. system or hard drive with specific user ID, where neural network access the samples of user by its ID which is the 1) Signature Area: It is signature’s normalized area. folder name in Hard drive which is considered as database in Ratio of area that signature has occupies by pixels of this system. signature in the bounding box is known as normalized area

2) Signature Ratio: Ratio of the range of x coordinates to the range of y coordinates is known as width to height ratio. To calculate the width to height ratio, use the following formula:

Width to Height Ratio = (Xmax - Xmin) / (Ymax - Ymin)

Where, Figure 4. Training Window Xmax and Xmin = Maximum & Minimum values of x In Fig. 4, the given window shows the process of input coordinates of non-zero pixels the user database ID (the folder name in Hard Drive) for While, access the no of samples that are pre-stored in database Ymax and Ymin = Maximum & Minimum values of y (Hard Drive) for neural network training, in the database coordinates of non-zero pixels each folder there are at least 10 samples (signature images of user) per user are pre-stored for training purpose in PNG 3) Geometric center: The center of gravity is the 2-tuple (image file type) format. When a specific ID is inserted then (X, Y) and is given by: system will train given dataset in neural network training panel.

Where, X and Y denote the column number and row number of ON pixels (value 1) respectively.

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University of Sindh Journal of Information and Communication Technology (USJICT), Vol.2(1), pg:73-80

TABLE IV. CAR OF SAMPLE SIGNATURES User ID User Sample Acceptance Rate Remarks

1 99.93 Good

2 94.15 Better

3 99.55 Good

4 97.96 Good

5 97.24 Good

6 98.86 Good

7 92.94 Better

8 96.37 Good

9 95.09 Better

10 99.62 Good

V. CONCLUSION In this study, Artificial Neural Network based off-Line Signature Recognition and Verification System is presented. Training of the system is a necessary part as the success rate depends on the appropriate training sample. The success rate

of SRVS system is found approximately 95% (on average). Image quality plays an important role as poor quality of Figure 5. Result Window signature image may lead to the failure to recognize/verify a signature. Increase in the attributes/ features of signature will In Neural network training panel, we have set the input increase the verification ability of the system but it may lead nodes as 256 because neural network take input in the form to higher computational complexity. of nodes, at Hidden layer nodes are process (applied preprocessing steps and features extraction techniques here) REFERENCES and classified in 20 nodes, which are resultant nodes from these nodes our required output (average results are stored in [1] L. Nanni, E. Maiorana, A. Lumini, P. CampisiBasavaraj, L., temporary classes for comparison at run time) is generate. Sudhaker Samuel, R. D., "Offline-line Signature Verification and After completion of neural network training it directly goes Recognition: An Approach Based on Four Speed Stroke Angle", to verification window. In this window user input a single International Journal of Recent Trends in Engineering, vol. 2, no. 3, signature for verification purpose which may belong to November 2009. original person or not after input, the given signature image [2] Hanmandlu, M. , Hafizuddin, M. , Yusofb, M., Madasuc, V. K. ,"Off- will go to the process of preprocessing steps and features line signature verification and forgery detection using Fuzzy modeling", Elsevier, 2004. extraction techniques after that it will we compared with [3] Zois, Elias N., Linda Alewijnse, and George Economou. "Offline training dataset which are already trained and stored in signature verification and quality characterization using poset- temporary classes. After comparison, results are shown (refer oriented grid features." Pattern Recognition 54 (2016): 162-177. to Fig. 5). [4] Shah, Abdul Salam, M. N. A. Khan, Fazli Subhan, Muhammad If result of ID is below 85% then it will be considered as Fayaz, and Asadullah Shah. "An Offline Signature Verification False and it does not belong to original person. Technique Using Pixels Intensity Levels." International Journal of Following table IV is presenting the results as Correct Signal Processing, Image Processing and Pattern Recognition 9, no. 8 Acceptance Rate (CAR) that is the acceptance measurement (2016): 205-222. [5] K. Harika, and T.C.S. Ready, “A tool for robust offline signature factor from sample storage file. Samples are of different verification,” International journal of advanced research in computer types and each of them shows percentage of and communication engineering, vol.2, pp. 3417–3420, September matching/acceptance rate. 2013. [6] S. Odeh, and M. Khalil, “Apply multi-layer perceptron neural network for off-line signature verification and recognition,” IJCSI International Journal of Computer Science Issues, vol.8, pp. 261–266, November 2011.

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[7] R. Anjali, and M.R. Mathew, “An efficient approach to offline [25] Kumar, Ravinder, and Poonam Singhal, “Review on offline Signature signature verification based on neural network,” IJREAT verification by SVM”, 2017 International Journal of Research in Engineering & Advanced Technology, vol.1, pp. 1–5, June-July 2013 [8] Kumar, Pradeep, Shekhar Singh, Ashwani Garg, and Nishant Prabhat. "Hand written signature recognition & verification using neural network." International Journal of Advanced Research in Computer Science and Software Engineering 3, no. 3 (2013). [9] Sanmorino, Ahmad, and Setiadi Yazid. "A survey for handwritten signature verification." In Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on, pp. 54- 57. IEEE, 2012. [10] O. Abikoye, M. Mabayoje, and R. Ajibade, "Offline signature recognition & verification using neural network," International Journal of Computer Applications, vol. 35, pp. 44-51, 2011. [11] A. Pansare and S. Bhatia, "Handwritten Signature Verification using Neural Network," International Journal of Applied Information Systems, vol. 1, pp. 44-49, 2012. [12] M. V. Pandey and M. S. Shantaiya, "Signature verification using morphological features based on artificial neural network," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, 2012. [13] P. Shikha and S. Shailja, "Neural Network Based Offline Signature Recognition and Verification System," Research Journal of Engineering Sciences ISSN, vol. 2278, p. 9472, 2013. [14] Karki, Maya V., K. Indira, and S. Sethu Selvi. "Off-line signature recognition and verification using neural network." In Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on, vol. 1, pp. 307-312. IEEE, 2007. [15] C. Oz, F. Erçal, and Z. Demir, "Signature recognition and verification with ANN," in Proceeding of the Third International Conference on Electrical and Electronics Engineering, 2003. [16] F. Vargas, M. A. Ferrer, C. M. Travieso, and J. B. Alonso, "Off-line Handwritten Signature GPDS-960 Corpus," in ICDAR, 2007, pp. 764-768. [17] F. J. Zareen and S. Jabin, "A comparative study of the recent trends in biometric signature verification," in Contemporary Computing (IC3), 2013 Sixth International Conference on, 2013, pp. 354-358. [18] Ferrer, Miguel, Jesus B. Alonso, and Carlos M. Travieso. "Offline geometric parameters for automatic signature verification using fixed- point arithmetic."Pattern Analysis and Machine Intelligence, IEEE Transactions on 27, no. 6 (2005): 993-997. [19] V. Shah, U. Sanghavi, and U. Shah, "Off-line signature verification using curve fitting algorithm with neural networks," in Advances in Technology and Engineering (ICATE), 2013 International Conference on, 2013, pp. 1-5. [20] K. Lakshmi and S. Nayak, "Off-line signature verification using Neural Networks," in Advance Computing Conference (IACC), 2013 IEEE 3rd International, 2013, pp. 1065-1069. [21] Odeh, Suhail M., and Manal Khalil. "Off-line signature verification and recognition: Neural Network Approach." In Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on, pp. 34-38. IEEE, 2011 [22] Paigwar, S., & Shukla, S.,” Neural Network Based Offline Signature Recognition and Verification System”, Department of Electrical Engineering, Jabalpur Engineering College Jabalpur, MP, INDIA, Research Journal of Engineering Sciences Vol. 2(2), 11-15, February (2013) [23] Choudhary, N. Y., Mrs. Patil, R., Dr. Bhadade, U. Prof. B. M Chaudhari “Signature Recognition & Verification System Using Back Propagation Neural Network”, International Journal of IT, Engineering and Applied Sciences Research (IJIEASR), ISSN: 2319- 4413 Volume 2, No. 1, January 2013. [24] Malekian, V., Aghaei, A., Rezaeian, M., Alian, M. "Rapid Off-line Signature Verification Based on Signature Envelope and Adaptive Density Partitioning", IEEE, 2013.

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January 2018)

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January 2018)

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University of Sindh Journal of Information and Communication Technology (USJICT) Volume-2, Issue-1 (January 2018)

REFERENCES List and number all bibliographical references in 9-point Times, single-spaced, at the end of your paper. When referenced in the text, enclose the citation number in square brackets, for example [1]. The sentence punctuation follows the bracket [2]. Refer simply to the reference number, as in [3]—do not use “Ref. [3]” or “reference [3]” except at the beginning of a sentence: “Reference [3] was the first . . .” Unless there are six authors or more give all authors’ names; do not use “et al.”. Papers that have not been published, even if they have been submitted for publication, should be cited as “unpublished” [4]. Papers that have been accepted for publication should be cited as “in press” [5].

Article in a journal:

[1] D. Kornack and P. Rakic, “Cell Proliferation without Neurogenesis in Adult Primate Neocortex,” Science, vol. 294, Dec. 2001, pp. 2127- 2130, doi:10.1126/science.1065467.

[2] K. Elissa, “Title of paper if known,” unpublished.

[3] R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.

Article in a conference proceedings:

[4] H. Goto, Y. Hasegawa, and M. Tanaka, “Efficient Scheduling Focusing on the Duality of MPL Representatives,” Proc. IEEE Symp. Computational Intelligence in Scheduling (SCIS 07), IEEE Press, Dec. 2007, pp. 57-64, doi:10.1109/SCIS.2007.357670.

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Call for Papers

ISSN (P): 2521-5582 ISSN (E): 2523-1235 The Editorial team of University of Sindh Journal of Information and Communication Technology (USJICT) is pleased to invite you to submit your quality paper in its Forthcoming Issue upcoming Volume-2, Issue-2 in April 2018. Volume-2 Issue-3 July 2018 University of Sindh Journal of Information and Communication Technology (USJICT) is an open-access, double blind peer reviewed research journal, published quarterly by Submission By: Office of Dean Faculty of Natural Sciences and Institute of Information and 1st May, 2018 Communication Technology, University of Sindh, Jamshoro. The journal covers a full spectrum of specialized domains in Information Technology, Software Engineering, For enquires and assistance email to Computer Science, Electronics and Telecommunication. It includes original research editor.usjict@usind articles, review articles, case reports and scientific findings. The journal strictly follows the h.edu.pk guidelines proposed by Higher Education Commission (HEC) Pakistan. Salient Features All published papers are abstracted and indexed in Google Scholar, ArXiv Cornell University, University of Illinois - uiuc oai registry, BASE (Bielefeld Academic Search Engine), • International Academia.org, ResearchGate, Scribd, Scientific Indexing Services(SIS), EMBASE, EBSCO, Quality and Standards Ulrich's Knowledgebase, and many more… • Double-Blind Peer Reviewed • 03 Reviewers for Authors must submit their articles via Online Submission System (submit). each article • Quick Review Process Visit the official website for furthers details regarding scope and submission • Online Submission guidelines. and processing • Open Access – Free to All URL: http://sujo.usindh.edu.pk/index.php/USJICT/ • Publication within a Reasonable Looking forward to receiving your feedback and research manuscripts. Short Period • Effective Editorial and Reviewer With Best Wishes Standards • Both Online and Print Version Editor • Prompt Email Dr. Zeeshan Bhatti Notification Assistant Professor • No Application Institute of Information and Communication Technology Process and Publication Charges (Free to Co-Editor(s) Publish and Prof. Dr. Imdad Ali Ismaili Read) Director and Professor • Officially published by Institute of Information and Communication Technology University of Sindh, Jamshoro, Prof. Dr. Lachhman Das Dhomeja Professor Institute of Information and Communication Technology

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