Mobile Cloud Computing: Architectures, Algorithms and Applications Covers the Latest Technological and Architectural Advances in MCC
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Mobile Systems
CS 318 Principles of Operating Systems Fall 2017 Lecture 21: Mobile Systems Ryan Huang 11/30/17 CS 318 – Lecture 21 – Mobile Systems 2 Apply the security update immedidately! CS 318 – Lecture 21 – Mobile Systems Administrivia • Lab 4 deadline one week away • Groups of 2 students receive 2-day extra late hour • Groups of 3 students with 1 318 section student receive 1-day extra late-hour • Please, please don’t cheat • Homework 5 is released 11/30/17 CS 318 – Lecture 21 – Mobile Systems 4 Mobile Devices Become Ubiquitous Worldwide Devices Shipments by Device Type (Millions of Units) 3000 2500 2000 1500 1806.96 1879 1910 1959 1000 500 Google Nexus 6P 209.79 226 196 195 296.13 277 246 232 0 2013 2014 2015 2016 Traditional PCs Ultramobiles (Premium) Ultramobiles (Basic and Utility) Mobile Phones 5 History of Mobile OS (1) • Early “smart” devices are PDAs (touchscreen, Internet) • Symbian, first modern mobile OS - released in 2000 - run in Ericsson R380, the first ‘smartphone’ (mobile phone + PDA) - only support proprietary programs 11/30/17 CS 318 – Lecture 21 – Mobile Systems 6 History of Mobile OS (2) • Many smartphone and mobile OSes followed up - Kyocera 6035 running Palm OS (2001) • 8 MB non-expandable memory - Windows CE (2002) - Blackberry (2002) • was a prominent vendor • known for secure communications - Moto Q (2005) - Nokia N70 (2005) • 2-megapixel camera, bluetooth • 32 MB memory • Symbian OS • Java games 11/30/17 CS 318 – Lecture 21 – Mobile Systems 7 One More Thing… • Introduction of iPhone (2007) - revolutionize the smartphone industry - 4GB flash memory, 128 MB DRAM, multi-touch interface - runs iOS, initially only proprietary apps - App Store opened in 2008, allow third party apps 11/30/17 CS 318 – Lecture 21 – Mobile Systems 8 Android – An Unexpected Rival of iPhone • Android Inc. -
SON Functions for Multi-Layer LTE and Multi-RAT Networks
INFSO-ICT-316384 SEMAFOUR D4.1 SON functions for multi-layer LTE and multi-RAT networks (first results) Contractual Date of Delivery to the EC: November 30th, 2013 Actual Date of Delivery to the EC: November 29th, 2013 Work Package WP4 Participants: NSN-D, EAB, iMinds, FT, TNO, TUBS, NSN-DK Authors Daniela Laselva, Zwi Altman, Irina Balan, Andreas Bergström, Relja Djapic, Hendrik Hoffmann, Ljupco Jorguseski, István Z. Kovács, Per Henrik Michaelsen, Dries Naudts, Pradeepa Ramachandra, Cinzia Sartori, Bart Sas, Kathleen Spaey, Kostas Trichias, Yu Wang Reviewers Thomas Kürner, Kristina Zetterberg Estimated Person Months: 53 Dissemination Level Public Nature Report Version 1.0 Total number of pages: 141 Abstract: Five promising SON functionalities for future multi-RAT and multi-layer networks have been identified within the SEMAFOUR project, namely, Dynamic Spectrum Allocation and Interference Management, multi-layer LTE / Wi-Fi Traffic Steering (TS), Idle Mode mobility Handling, tackling the problem of High Mobility users and Active/reconfigurable Antenna Systems. This document covers the detailed description of the SON features, the Controllability and Observability analysis as well as initial directions of the SON design for the above-mentioned five use cases. Furthermore, initial performance evaluation in the realistic “Hannover scenario” is provided for the proposed TS SON algorithms. Keywords: Self-management, Self-optimization, SON, Multi-layer, Multi-RAT, Spectrum Allocation, Traffic steering, High Mobility, Active Antenna, Controllability and Observability SEMAFOUR (316384) D4.1 SON functions (first results) Executive Summary Self-management and self-optimization will play critical roles in the future evolution of wireless networks. The complexity of future multi-layer / RAT technologies and the pressure to be competitive, e.g. -
A Techno-Economic Comparison Between Outdoor Macro-Cellular and Indoor Offloading Solutions
A techno-economic comparison between outdoor macro-cellular and indoor offloading solutions FEIDIAS MOULIANITAKIS Master's Degree Project Stockholm, Sweden August 2015 TRITA-ICT-EX-2015:224 A techno-economic comparison between outdoor macro-cellular and indoor offloading solutions Feidias Moulianitakis 2015-08-31 Master’s Thesis Examiner Academic adviser Prof. Jan Markendahl Ashraf A. Widaa Ahmed School of Information and Communication Technology (ICT) KTH Royal Institute of Technology Stockholm, Sweden Abstract Mobile penetration rates have already exceeded 100% in many countries. Nowadays, mobile phones are part of our daily lives not only for voice or short text messages but for a plethora of multimedia services they provide via their internet connection. Thus, mobile broadband has become the main driver for the evolution of mobile networks and it is estimated that until 2018 the mobile broadband traffic will exceed the level of 15 exabytes. This estimation is a threat to the current mobile networks which have to significantly improve their capacity performance. Furthermore, another important aspect is the fact that 80% of the mobile broadband demand comes from indoor environments which add to the signal propagation the burden of building penetration loss. Keeping these facts in mind, there are many potential solutions that can solve the problem of the increasing indoor mobile broadband demand. In general, there are two approaches; improve the existing macro-cellular networks by for example enhancing them with carrier aggregation or enter the buildings and deploy small cell solutions such as femtocells or WiFi APs. Both the academia and the industry have already shown interest in these two approaches demonstrating the importance of the problem. -
Mobile Data Offloading – Challenges and Solution
International Journal of Control and Automation Vol. 12, No. 04, (2019), pp. 221-228 Mobile Data Offloading – Challenges and Solution 1 2 3 4 Aradhana , Dr. Samarendra Mohan Ghosh , Sudha Tiwari , Smita Suresh Daniel 1Research Scholar in Dr. C.V. Raman University Bilaspur, 2Professor in Dr. C.V. Raman University Bilaspur, 3Assistant Prof. in Rungta College of Engineering and Technology Bhilai, 4 Assistant Professor in Sat. Thomas College Bhilai. Abstract Mobile cloud computing is emerging at the rapid rate with evolutions in IT industries but various challenges are also coming up during data and task migration from mobile devices to cloud. Major issue confronted by mobile cloud computing is security of data during offloading It requires awareness of growing security threats associated with data offloading and strategic planning to resolve the issue. In this paper, we bring out various security issues subjected to mobile data offloading and challenges during computational data offloading on clouds. By virtue of our study on mobile cloud computing and its related threats and the avenues to tackle these issues we developed an agent based framework for data offloading in a secure and energy efficient manner. Using this proposed framework we can enhance the level of security and use this as a tool to address many security issues arising during computational data offloading from mobile to cloud. Keywords: Code Obfuscation, Encryption, Computation Offloading, IaaS, PaaS, SaaS. 1. Introduction Mobile Cloud Computing is a phenomena of migrating the computational process to server that executes some events or application on behalf of the user mobile, but it may suffer many security challenges like authentication, code integrity, access control, availability, anti- tempering and trust management. -
UNIVERSITY of PIRAEUS DEPARTMENT of DIGITAL SYSTEMS POSTGRADUATE PROGRAMME Economic Management and Digital Systems Security
UNIVERSITY OF PIRAEUS DEPARTMENT OF DIGITAL SYSTEMS POSTGRADUATE PROGRAMME Economic Management and Digital Systems Security Smartphone Forensics & Data Acquisition DISSERTATION Pachigiannis Panagiotis MTE1219 2015 Contents Contents……… ....................................................................................................................................... 2 Acknowledgement ................................................................................................................................... 7 Abstract……. ........................................................................................................................................... 8 1) Introduction........................................................................................................................................... 9 1.1) Context ........................................................................................................................................... 9 1.2) Aim & Objective .......................................................................................................................... 10 1.3) Background .................................................................................................................................. 11 1.4) Structure of Thesis ....................................................................................................................... 11 2) Mobile Devices .................................................................................................................................. -
Performance Analysis of Mobile Data Offloading in Heterogeneous
1 Performance Analysis of Mobile Data Offloading in Heterogeneous Networks Fidan Mehmeti, Student Member, IEEE, and Thrasyvoulos Spyropoulos, Member, IEEE Abstract—An unprecedented increase in the mobile data traffic volume has been recently reported due to the extensive use of smartphones, tablets and laptops. This is a major concern for mobile network operators, who are forced to often operate very close to (or even beyond) their capacity limits. Recently, different solutions have been proposed to overcome this problem. The deployment of additional infrastructure, the use of more advanced technologies (LTE), or offloading some traffic through Femtocells and WiFi are some of the solutions. Out of these, WiFi presents some key advantages such as its already widespread deployment and low cost. While benefits to operators have already been documented, it is less clear how much and under what conditions the user gains as well. Additionally, the increasingly heterogeneous deployment of cellular networks (partial 4G coverage, small cells, etc.) further complicates the picture regarding both operator- and user-related performance of data offloading. To this end, in this paper we propose a queueing analytic model that can be used to understand the performance improvements achievable by WiFi-based data offloading, as a function of WiFi availability and performance, user mobility and traffic load, and the coverage ratio and respective rates of different cellular technologies available. We validate our theory against simulations for realistic scenarios and parameters, and provide some initial insights as to the offloading gains expected in practice. Index Terms—Mobile data offloading, Queueing theory, Probability generating functions, HetNets. ✦ 1 INTRODUCTION ATELY, an enormous growth in the mobile data traffic A more cost-effective way of alleviating the problem of L has been reported. -
PDA Phone Choices
OK. You’ve finally broken down and gotten a cell phone with a color screen, camera and a ring that plays 1814 Overture. Your teen age child no longer sneers when you pull it out of your purse or pocket. You delude yourself into thinking that you have finally arrived. Well, get a grip, you technological slug, your phone is nothing. Nada. Zip, etc. A phone that won’t surf the internet, allow you to read books, have all of your Outlook contacts, tasks and calendar handy, and pull up web sites and your emails, with attachments, is pretty weak. And even though you finally managed to get a PDA that will do these things, you now have to worry about carrying and losing both. Enter smart phones. Actually they have been here for some time, but they have gotten smaller, better looking, have greater battery power, and will do lots of stuff. Better yet, the price points have moved considerably. Depending upon the wireless carrier, the prices of some of these phone/pda combinations are less than $300. When I last wrote about the Kyocera 6035 a couple of years ago, the price was $600. I was able to snatch a used one for $200, and have had no problems. But I have had complaints. My phone will pull my office email, but not the attachments. Since our work product usually resides in an attachment, this has occasionally created problems. And finding contacts with one hand, given my large office database, can sometimes be a challenge. But these problems have not been insurmountable, and the benefits have far outweighed the hassle factor. -
On Practical Aspects of Mobile Data Offloading to Wi-Fi Networks
On Practical Aspects of Mobile Data Offloading to Wi-Fi Networks Adnan Aijaz †, Nazir Uddin ‡, Oliver Holland †, and A. Hamid Aghvami † †Centre for Telecommunications Research, King’s College London, London WC2R 2LS, UK ‡Nokia Siemens Networks, Indonesia {adnan.aijaz, oliver.holland, hamid.aghvami}@kcl.ac.uk, [email protected] Abstract Data traffic over cellular networks is exhibiting an ongoing exponential growth, increasing by an order of magnitude every year and has already surpassed voice traffic. This increase in data traffic demand has led to a need for solutions to enhance capacity provision, whereby traffic offloading to Wi-Fi is one means that can enhance realised capacity. Though offloading to Wi-Fi networks has matured over the years, a number of challenges are still being faced by operators to its realization. In this article, we carry out a survey of the practical challenges faced by operators in data traffic offloading to Wi-Fi networks. We also provide recommendations to successfully address these challenges. Index Terms – mobile data offloading, Wi-Fi offloading, DAS, backhaul, 802.11u I. INTRODUCTION In recent years, data traffic transmitted over cellular/mobile networks has seen a continuous exponential growth increasing by an order of magnitude every year. According to Cisco forecasts [1], global mobile data traffic is expected to grow to 15.9 exabytes (1 exa = 10 18 ) per month by 2018, which is an 11-fold increase over 2013. This unprecedented growth of data traffic can be attributed to a number of factors. A first factor is the introduction of high- end devices such as smartphones, tablets, laptops, handheld gaming consoles, etc. -
2 Androidoverview.Pdf
CS371m - Mobile Computing Android Overview and Android Development Environment What is Android? • A software stack for mobile devices that includes – An operating system – Middleware – Key Applications • Uses Linux to provide core system services – Security – Memory management – Process management – Power management – Hardware drivers http://developer.android.com/guide/basics/what-is-android.html Android Versioning • On the order of 25 versions in 8 years. • Slowing down, current pace is one large, major release a year – will this slow down more? • Android releases have a code name, version number, and API level • Most recent: – Nougat, Version 7.1, API level 25 • https://en.wikipedia.org/wiki/Android_version_history A Short History Of Android • 2001 Palm Kyocera 6035, combing PDA and phone – PDA = personal data assistant, PalmPilot • 2003 - Blackberry smartphone released • 2005 – Google acquires startup Android Inc. to start Android platform. – Work on Dalvik VM begins • 2007 – Open Handset Alliance announced – Early look at SDK – June, iPhone released • 2008 – Google sponsors 1st Android Developer Challenge – T-Mobile G1 announced, released fall – SDK 1.0 released – Android released open source (Apache License) – Android Dev Phone 1 released Pro Android by Hashimi & Komatineni (2009) Short History cont. • 2009 – SDK 1.5 (Cupcake) after Alpha and Beta • New soft keyboard with “autocomplete” feature – SDK 1.6 (Donut) • Support Wide VGA – SDK 2.0/2.0.1/2.1 (Eclair) • Revamped UI, browser • 2010 – Nexus One released to the public – SDK 2.2 (Froyo) • Flash support, tethering – SDK 2.3 (Gingerbread) • UI update, system-wide copy-paste https://en.wikipedia.org/wiki/Android_version_history Short History cont. • 2011 – SDK 3.0 (Honeycomb) for tablets only • New UI for tablets, support multi-core processors, fragments – SDK 3.1 and 3.2 • Hardware support and UI improvements – SDK 4.0 (Ice Cream Sandwich) • For Q4, combination of Gingerbread and Honeycomb 7 Short History cont. -
Traffic Offloading Through Wi-Fi Networks
International Journal of Computer Applications (0975 – 8887) Volume 178 – No. 21, June 2019 Traffic Offloading through Wi-Fi Networks Kritika Kapoor Assitant Professor Tramiet, Mandi ABSTRACT Cell systems are as of now confronting movement over- burden issue because of extreme developing interest of advanced mobile phones, tablets and portable PCs. The after effect of which top of the line gadgets twofold their information movement consistently and this pattern is relied upon to proceed with given the fast advancement of versatile social applications. This expansion in information movement is gauge to quicken and after effect of which they brought on a prompt requirement for offloading activity for ideal execution of both voice and information administrations. Subsequently, extraordinary inventive arrangements have risen to oversee information movement. A financially savvy and one of sensible arrangement is to offload cell movement through Wi-Fi, Femtocells or Delay Tolerant System (DTN) Figure 1: Global Mobile Data Traffic [1] to decrease the weight on the cell organizes and furthermore increment the quality of service (QoS). Wi-Fi offloading is a To handle this problem of explosive traffic demands growth procedure for tending to the portable information blast issue. and limited capacity of network there is urgent need to Offloading portable information activity through pioneering provide some better solution to reduce the traffic in the correspondences is answering for tackle this issue. The network. Various solutions has been provided to overcome thought is that the specialist organizations convey data over this traffic like installing new base stations (BS’s), increase cell systems to just clients in the objective set. -
Spatiotemporal Characterization of Contact Patterns in Dynamic Networks
Ref. Ares(2014)1430495 - 05/05/2014 Mobile Opportunistic Traffic Offloading (MOTO) D3.2: Spatiotemporal characterization of contact patterns in dynamic networks Document information Edition 13 Date 29/04/2014 Status Edition Editor UPMC Contributors UPMC, TCS, CNR, INNO D3.2 Spatiotemporal characterization of contact patterns in dynamic networks Contents 1 Executive summary 4 2 Introduction 4 3 Contact patterns under a unifying framework 5 3.1 The SPoT Mobility Framework . .5 3.1.1 The social and spatial dimensions of human mobility . .6 3.1.2 From meeting places to geographical locations . .7 3.1.3 The temporal dimension of user visits to meeting places . .8 3.2 Analysis of real user movements . .9 3.3 Testing the framework flexibility . 10 3.4 Testing the framework controllability . 10 3.4.1 Validation . 11 3.5 Final remarks . 12 4 Impact of duty cycling on contact patterns 13 4.1 Problem statement . 13 4.2 The case of exponential intercontact times . 14 4.2.1 Computing N ....................................... 14 4.2.2 Computing the detected intercontact times . 15 4.2.3 Validation . 15 4.3 The effect of duty cycling on the delay . 17 4.4 Energy, traffic, and network lifetime . 17 4.5 Final remarks . 20 5 Contacts and intercontacts beyond one hop 20 5.1 Defining a new vicinity for opportunistic networks . 21 5.2 The limits of the binary assertion . 23 5.2.1 Datasets . 23 5.2.2 Binary assertion illustration . 25 5.2.3 Missed transmission possibilities . 25 5.3 κ-vicinity analysis . 26 5.3.1 The seat of κ-vicinities: connected components . -
Data Traffic Offload from Mobile to Wi-Fi Networks: Behavioural Patterns of Smartphone Users
Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 2608419, 13 pages https://doi.org/10.1155/2018/2608419 Research Article Data Traffic Offload from Mobile to Wi-Fi Networks: Behavioural Patterns of Smartphone Users Siniša Husnjak , Dragan PerakoviT , and Ivan Forenbacher Faculty of Transport and Trafc Sciences, University of Zagreb, Vukeli´ceva 4, 10000 Zagreb, Croatia Correspondence should be addressed to Siniˇsa Husnjak; [email protected] Received 11 August 2017; Revised 14 January 2018; Accepted 12 February 2018; Published 25 March 2018 Academic Editor: Michael McGuire Copyright © 2018 Siniˇsa Husnjak et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper presents a model for defning the behavioural patterns of smartphone users when ofoading data from mobile to Wi-Fi networks. Te model was generated through analysis of individual characteristics of 298 smartphone users, based on data collected via online survey as well as the amount of data ofoaded from mobile to Wi-Fi networks as measured by an application integrated into the smartphone. Users were segmented into categories based on data volume ofoaded from mobile to Wi-Fi networks, and numerous user characteristics were explored to develop a model capable of predicting the probability that a user with given characteristics will fall into a given category of data ofoading. Tis model may prove useful for analysing smartphone user behaviour when ofoading data. 1. Introduction [5] concludes that the amount of smartphone-based data trafc ofoading to Wi-Fi networks will be 56% of total data Te number of smartphone subscription connections at trafc by the year 2020, and studies have claimed that 65% the global level has reached 3 billion, and in the last fve [14] or more than 70% [15] of smartphone data trafc is being years, data trafc has increased more than 40-fold [1–4].