DOCSLIB.ORG

A Prototype of EEG System for Iot

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Prototype of EEG System for Iot 2nd Reading May 2, 2020 16:12 2050018 International Journal of Neural Systems, Vol. 30, No. 7 (2020) 2050018 (15 pages) c The Author(s) DOI: 10.1142/S0129065720500185 A Prototype of EEG System for IoT Francisco Laport, Adriana Dapena∗,PaulaM.Castro, Francisco J. Vazquez-Araujo and Daniel Iglesia Department of Computer Engineering CITIC Research Center & University of A Coru˜na Campus de Elvi˜na, A Coru˜na 15071, Spain ∗[email protected] Accepted 5 February 2020 Published Online 4 May 2020 In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simulation results show both classifier accuracies and their corresponding system delays. Keywords: Electroencephalography; Internet of Things; prototype; signal processing. 1. Introduction NeuroSky Mindwave.12 However, both the Emotiv or NeuroSky devices require the use of accompany- Brain–Computer Interfaces (BCI) are communica- ing proprietary software. tion systems that monitor the cerebral activity and In this work, we present a prototype of an EEG translate certain characteristics, corresponding to 1 device designed to acquire signals using a reduced user intentions, to commands for device control. Int. J. Neur. Syst. Downloaded from www.worldscientific.com number of sensors. The system is composed of low- One of the greatest challenges of BCI technology cost components, including a dual core microcon- is the development of wearable and portable elec- troller that allows us to perform all operations asso- trode devices that are minimally invasive and ready- ciated to signal classification simultaneously to the to-use with short training times.2,3 In particular, transmission of data to the Internet of Things (IoT) current research is focused on the potential of Elec- environment. Our first objective is the assessment of troencephalography (EEG)4–6 techniques to capture by 213.60.74.193 on 06/01/20. Re-use and distribution is strictly not permitted, except for Open Access articles. its performance in determining the user’s eye states, the brain activity associated to user intent.7–9 This i.e, open eyes (oE) or closed eyes (cE), using differ- requires the design and development of friendly ent strategies based on short training times. These devices with real-time operation and adaptation for strategies are as follows: people with motor functional diversity. Recently, dif- ferent companies and studies have presented com- (1) Real-valued instead of complex-valued trans- mercial products with the aim of opening the EEG forms, since, in general, the use of complex- technology to applications not restricted to medical 2,10 11 valued signals requires more than twice the num- diagnosis, such as the Emotiv Epoc and the ber of computational operations. ∗ Corresponding author. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 2050018-1 2nd Reading May 2, 2020 16:12 2050018 F. Laport et al. (2) Sliding windows with overlapping instead of the caretakers. In a more recent study with the same traditional approach based on nonoverlapped headset, Narayana et al.19 present a BCI running on windows, thus reducing delays between acquisi- an Android phone which locks/unlocks a wheelchair tion and decision. and controls its movements through an IoT environ- (3) A classifier based either on a threshold-based ment. Lee et al.20 present a BCI-controlled mobile method or on Linear Discriminant Analysis robot for telepresence which is developed in an IoT (LDA). system and used for the communication between sub- (4) A correction system for the mitigation of decision ject and robot. errors. In previous work, we have studied the control of IoT devices using the detection of cE and oE Our second objective is the use of the acquired infor- states.21 For the detection of each state, we ana- mation about the user’s eye state for the control of lyzed the alpha and beta waves of the EEG signal. IoT devices by means of ON/OFF operations. Alpha waves, whose frequency ranges from 8 Hz to This paper is organized as follows. Section 2 sum- 13 Hz, are associated with the most relaxed and sta- marizes some of the most important previous works ble brain state; whereas beta waves, in the frequency related to the utilization of EEG for IoT and to the rangefrom13Hzto22Hz,areassociatedwithstates classification of cE and oE states from acquired sig- of high wakefulness.22–25 nals. Section 3 describes our open source device for Both the eye state identification and the eye- EEG signal acquisition and an open solution from gaze analysis have become an active research field OpenBCI.13 Section 4 shows the architecture used during the last years due to their implication in to integrate the EEG device with IoT elements. Sec- human–machine interfaces.26,27 In particular, EEG tion 5 explains the proposed method for cE and oE eye state detection has been successfully applied in classification. The methodology and materials used a wide variety of domains,28 such as driving drowsi- for the experiments are presented in Sec. 6. Section 7 ness detection,29 infant sleep-waking state classifi- shows the results obtained from a set of test individ- cation30 and stress features identification,31 among uals. Section 8 analyzes these results. Finally, Sec. 9 others. Several papers have associated cE and oE contains the more relevant conclusions of this work. with particular frequency and amplitude ranges of the brain waves.22–25 For instance, Kirkup et al.32 2. Related Work present a home automation control system for a rapid The IoT paradigm refers to a global dynamic infras- on/off switch appliance which filters the EEG signal Int. J. Neur. Syst. Downloaded from www.worldscientific.com tructure based on interoperable communication pro- between 8 Hz and 12 Hz and employs the resulting tocols that integrate physical and virtual objects alpha values to determine the user’s eye state based into an information network.14 Among other applica- on a threshold. tions, home automation or smart homes are rapidly In a more recent study, Saghafi et al. propose gaining interest.15,16 The aim of such systems is to the use of delta and theta bands for these purposes, make the home environment not only comfortable applying an 8 Hz low-pass filter to the EEG signal33 by 213.60.74.193 on 06/01/20. Re-use and distribution is strictly not permitted, except for Open Access articles. and accessible, but also to optimize and automate and using different methods for the extraction of the the use of appliances like TV sets, air conditioners, principal features in eye state classifiers such as, for light bulbs, ovens or washing machines. example, Multivariate Empirical Mode Decomposi- The development of low-cost EEG devices as tion (MEMD), Logistic Regression (LR), Artificial a tool for IoT has been presented in some recent Neural Networks (ANN) or Support Vector Machine papers.17 For instance, Jadadish et al. present a (SVM). Their proposed algorithm detected the eye BCI application which detects voluntary blinks of state with an accuracy of 88.2% in less than 2 s. the user and controls electrical appliances included On the other hand, Naderi et al.34 propose a in an IoT system. Mathe et al.18 connect a BCI technique based on Welch’s method for estimating application which employs the Mindwave12 consumer the Power Spectral Density (PSD) and on Recurrent EEG device to estimate the user’s depression level Neural Networks (RNNs) to differentiate a relaxed with an IoT ecosystem responsible for notifying the and open eye state from an epileptic seizure. Features 2050018-2 2nd Reading May 2, 2020 16:12 2050018 A Prototype of EEG System for IoT from EEG time series and PSD levels were extracted 3. Open Devices to train and test the classifier which, at the end, The elements of the EEG device we developed are exhibited an accuracy of 100%. In another study on shown in Fig. 1. This prototype has a total of three the same dataset, Acharya et al.35 propose the use of sensors: input, reference and ground. Convolutional Neural Networks (CNN) for the devel- The input signal is amplified and bandpass fil- opment of a Computer-Aided-Diagnosis (CAD) sys- tered between 4.7–29.2 Hz. We use an AD8221 instru- tem that automatically detects seizure using EEG mentation amplifier followed by: (i) a 50 Hz notch signals. They implement an algorithm based on a filter to avoid the interference of electric devices in 13-layer deep convolutional neural network for the the vicinity of the sensor wires; (ii) a second-order detection of normal, preictal, and seizure classes, low-pass filter; (iii) a second-order high-pass filter; where the normal state corresponds to a relaxed and (iv) a final bandpass filter with adjustable gain. and CE situation. Their proposed technique achieves The different filter stages that follow the instrumen- . an accuracy, specificity, and sensitivity of 88 67%, tation amplifier are shown in Fig.
Load more

Recommended publications
  • Internet of Things Meets Brain-Computer Interface: a Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity
    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity Xiang Zhang, Student Member, IEEE, Lina Yao, Member, IEEE, Shuai Zhang, Student Member, IEEE, Salil Kanhere, Member, IEEE, Michael Sheng, Member, IEEE, and Yunhao Liu, Fellow, IEEE Abstract—A Brain-Computer Interface (BCI) acquires brain signals, analyzes and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, pre-processing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning based Selective Attention Mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified Long Short-Term Memory (LSTM) to distinguish the inter-dimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines.
    [Show full text]
  • The Internet of Things (Iot): an Overview
    Updated February 12, 2020 The Internet of Things (IoT): An Overview The Internet of Things (IoT) is a system of interrelated incorporation of IIoT and analytics is viewed by experts as devices connected to a network and/or to one another, the Fourth Industrial Revolution, or 4IR. exchanging data without necessarily requiring human-to- machine interaction. In other words, IoT is a collection of Internet of Medical Things (IoMT): The healthcare field electronic devices that can share information among has begun incorporating IoT, creating the Internet of themselves. Examples include smart factories, smart home Medical Things (IoMT). These devices, such as heart devices, medical monitoring devices, wearable fitness monitors and pace makers, collect and send patient health trackers, smart city infrastructures, and vehicular statistics over various networks to healthcare providers for telematics. Potential issues for Congress include regulation, monitoring, analysis, and remote configuration. At a digital privacy, and data security as discussed below. personal health level, wearable IoT devices, such as fitness trackers and smart watches, can track a user’s physical IoT Characteristics activities, basic vital data, and sleeping patterns. According IoT devices are often called “smart” devices because they to a 2019 survey by Pew Research, about one-in-five have sensors and can conduct complex data analytics. IoT Americans uses a smart watch or fitness tracker. devices collect data using sensors and offer services to the user based on the analyses of that data and according to Smart Cities: IoT devices and systems in the utilities, user-defined parameters. For example, a smart refrigerator transportation, and infrastructure sectors may be grouped uses sensors (e.g., cameras) to inventory stored items and under the category of “smart city.” Utilities can use IoT to can alert the user when items run low based on image create “smart” grids and meters for electricity, water, and recognition analyses.
    [Show full text]
  • Fog Computing: a Platform for Internet of Things and Analytics
    Fog Computing: A Platform for Internet of Things and Analytics Flavio Bonomi, Rodolfo Milito, Preethi Natarajan and Jiang Zhu Abstract Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disrup- tions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge. 1 Introduction The “pay-as-you-go” Cloud Computing model is an efficient alternative to owning and managing private data centers (DCs) for customers facing Web applications and batch processing. Several factors contribute to the economy of scale of mega DCs: higher predictability of massive aggregation, which allows higher utilization with- out degrading performance; convenient location that takes advantage of inexpensive power; and lower OPEX achieved through the deployment of homogeneous compute, storage, and networking components. Cloud computing frees the enterprise and the end user from the specification of many details. This bliss becomes a problem for latency-sensitive applications, which require nodes in the vicinity to meet their delay requirements. An emerging wave of Internet deployments, most notably the Internet of Things (IoTs), requires mobility support and geo-distribution in addition to location awareness and low latency. We argue that a new platform is needed to meet these requirements; a platform we call Fog Computing [1]. We also claim that rather than cannibalizing Cloud Computing, F. Bonomi R.
    [Show full text]
  • The Internet of Things: Impact on Public Safety Communications
    Cybersecurity and Infrastructure Security Agency The Internet of Things March 2019 The Internet of Things: Impact on Public Safety Communications The Internet of Things (IoT) is the network of physical devices and connectivity that enables objects to connect to one another, to the Internet, and exchange data amongst themselves.1, 2 IoT allows connected devices to be sensed or controlled remotely across network infrastructures, creating opportunities for more direct, cross-platform integration and improved efficiencies for the transfer of data between devices. IoT presents undeniable implications for public safety IoT goes beyond simply connecting communications. In turn, comprehensively addressing the objects to the Internet; it allows ever-growing IoT environment presents a unique challenge to physical objects to intelligently self- service providers, equipment manufacturers, and consumers. identify and communicate with other Harnessing network architecture changes and equipping devices, creating a new model of everyday objects to be IoT-enabled will allow public safety information sharing with a variety of stakeholders to maximize existing infrastructure investments potential applications. and provide near-real time decision support experiences that can change how they operate. IoT Benefits IoT-enabled devices can provide numerous benefits to public safety, as shown in Table 1. For example, a traffic accident response team could use the data collected from a variety of Internet-connected devices ― such as the involved vehicles (e.g.,
    [Show full text]
  • Home Automation System Using Google Assistant
    © 2021 JETIR June 2021, Volume 8, Issue 6 www.jetir.org (ISSN-2349-5162) HOME AUTOMATION SYSTEM USING GOOGLE ASSISTANT MUDASIR M, NEHA V, NIHAAL THATHIR K, PAVAN YADAV M, POLLARPU SREERAMULU, SMITHA PATIL B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India B.Tech. Student, Department of Computer Science and Engineering, Presidency University, Bangalore, India Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bangalore, India Abstract: Nowadays Technology keeps on upgrading. The idea behind Google assistant-controlled Home automation is to control home devices with voice. On the market there are many devices available to do that, but making our own is awesome. In this project, the Google assistant requires voice commands. Adafruit account which is a cloud based free IoT web server used to create virtual switches, is linking to IFTTT website abbreviated as “If This Than That” which is used to create if else conditional statements. The voice commands for Google assistant have been added through IFTTT website. In this home automation, as the user gives commands to the Google assistant, Home appliances like Bulb, Fan and Motor etc., can be controlled accordingly. The commands given through the Google assistant are decoded and then sent to the microcontroller, the microcontroller in turn control the relays connected to it. The device connected to the respective relay can be turned ON or OFF as per the users request to the Google Assistant.
    [Show full text]
  • Predictive Analysis of 3D Reram-Based PUF for Securing the Internet of Things
    Predictive Analysis of 3D ReRAM-based PUF for Securing the Internet of Things Jeeson Kim Hussein Nili School of Engineering Electrical and Computer Engineering RMIT University University of California Santa Barbara Melbourne, Australia Santa Barbara, USA [email protected] [email protected] Gina C. Adam Nhan Duy Truong Electrical and Computer Engineering School of Engineering University of California Santa Barbara RMIT University Santa Barbara, USA Melbourne, Australia gina [email protected] [email protected] Dmitri B. Strukov Omid Kavehei Electrical and Computer Engineering Electrical and Information Engineering University of California Santa Barbara The University of Sydney Santa Barbara, USA Sydney, Australia [email protected] [email protected] Abstract—In recent years, an explosion of IoT devices challenge [2, 3]. Widely used traditional cryptographic and its use leads threats to the privacy and security solutions, for example, advanced encryption standard concerns of individual users and merchandises. As one (AES) and elliptic curve cryptography (ECC), can be of promising solutions, physical unclonable function (PUF) has been extensively studied. This paper investigates quality used for both the integrity and the authentication of of randomness in the first generation of 3D analog ReRAM exchanging data and messages. PUF primitives using measured and gathered data from IoT hardware anti-counterfeiting, integrated circuit fabricated ReRAM crossbars. This study is significant as (IC) trust and physical tampering are also critical the randomness quality of a PUF directly relates to its tasks [4]. In 2014, defense advanced research projects resilience against various model-building attacks, includ- ing machine learning attack.
    [Show full text]
  • Paving the Way for Self-Driving Vehicles”
    June 13, 2017 The Honorable John Thune, Chairman The Honorable Bill Nelson, Ranking Member U.S. Senate Committee on Commerce, Science & Transportation 512 Dirksen Senate Office Building Washington, DC 20510 RE: Hearing on “Paving the Way for Self-Driving Vehicles” Dear Chairman Thune and Ranking Member Nelson: We write to your regarding the upcoming hearing “Paving the Way for Self-Driving Vehicles,”1 on the privacy and safety risks of connected and autonomous vehicles. For more than a decade, the Electronic Privacy Information Center (“EPIC”) has warned federal agencies and Congress about the growing risks to privacy resulting from the increasing collection and use of personal data concerning the operation of motor vehicles.2 EPIC was established in 1994 to focus public attention on emerging privacy and civil liberties issues. EPIC engages in a wide range of public policy and litigation activities. EPIC testified before the House of Representatives in 2015 on “the Internet of Cars.”3 Recently, EPIC 1 Paving the Way for Self-Driving Vehicles, 115th Cong. (2017), S. Comm. on Commerce, Science, and Transportation, https://www.commerce.senate.gov/public/index.cfm/pressreleases?ID=B7164253-4A43- 4B70-8A73-68BFFE9EAD1A (June 14, 2017). 2 See generally EPIC, “Automobile Event Data Recorders (Black Boxes) and Privacy,” https://epic.org/privacy/edrs/. See also EPIC, Comments, Docket No. NHTSA-2002-13546 (Feb. 28, 2003), available at https://epic.org/privacy/drivers/edr_comments.pdf (“There need to be clear guidelines for how the data can be accessed and processed by third parties following the use limitation and openness or transparency principles.”); EPIC, Comments on Federal Motor Vehicle Safety Standards; V2V Communications, Docket No.
    [Show full text]
  • Internet of Things
    INTERNET OF THINGS Internet of Things (IoT) or smart devices refers to any object or device that is connected to the Internet. This rapidly expanding set of “things,” which can send and receive data, includes cars, appliances, smart watches, lighting, home assistants, home security, and more. #BeCyberSmart to connect with confidence and protect your interconnected world. WHY SHOULD WE CARE? • Cars, appliances, wearables, lighting, healthcare, and home security all contain sensing devices that can talk to another machine and trigger other actions. Examples include devices that direct your car to an open spot in a parking lot; mechanisms that control energy use in your home; and tools that track eating, sleeping, and exercise habits. • New Internet-connected devices provide a level of convenience in our lives, but they require that we share more information than ever. • The security of this information, and the security of these devices, is not always guaranteed. Once your device connects to the Internet, you and your device could potentially be vulnerable to all sorts of risks. • With more connected “things” entering our homes and our workplaces each day, it is important that everyone knows how to secure their digital lives. SIMPLE TIPS TO OWN IT. • Shake up your password protocol. Change your device’s factory security settings from the default password. This is one of the most important steps to take in the protection of IoT devices. According to NIST guidance, you should consider using the longest password or passphrase permissible. Get creative and create a unique password for your IoT devices. Read the Creating a Password Tip Sheet for more information.
    [Show full text]
  • Internet of Things Innovation Landscape Brief © Irena 2019
    INTERNET OF THINGS INNOVATION LANDSCAPE BRIEF © IRENA 2019 Unless otherwise stated, material in this publication may be freely used, shared, copied, reproduced, printed and/or stored, provided that appropriate acknowledgement is given of IRENA as the source and copyright holder. Material in this publication that is attributed to third parties may be subject to separate terms of use and restrictions, and appropriate permissions from these third parties may need to be secured before any use of such material. ISBN 978-92-9260-142-3 Citation: IRENA (2019), Innovation landscape brief: Internet of Things, International Renewable Energy Agency, Abu Dhabi. ACKNOWLEDGEMENTS This report was prepared by the Innovation team at IRENA’s Innovation and Technology Centre (IITC) with text authored by Sean Ratka, Arina Anisie, Francisco Boshell and Nadeem Goussous. This report benefited from the input and review of experts: Tiago Maouras (EDP), Stephen Woodhouse and Tom Ingelse (Poyry), Jaideep Sandhu (Engie), along with Emanuele Taibi, Elena Ocenic, Nina Litman-Roventa and Paul Komor (IRENA). Report available online: www.irena.org/publications For questions or to provide feedback: [email protected] DISCLAIMER This publication and the material herein are provided “as is”. All reasonable precautions have been taken by IRENA to verify the reliability of the material in this publication. However, neither IRENA nor any of its officials, agents, data or other third- party content providers provides a warranty of any kind, either expressed or implied, and they accept no responsibility or liability for any consequence of use of the publication or material herein. The information contained herein does not necessarily represent the views of all Members of IRENA.
    [Show full text]
  • Future-Proofing the Connected World: 13 Steps to Developing Secure Iot Products
    Future-proofing the Connected World: 13 Steps to Developing Secure IoT Products Presented by the IoT Working Group Table of Contents Forward Introduction Document Scope The Need for IoT Security IoT Products Can Compromise Privacy IoT products can lend their computing power to launch DDoS Attacks Medical Devices and Medical Standard Protocols are Vulnerable to Attack Drones Are Approaching Mainstream Status and Being Used as a Platform for Reconnaissance Critical national infrastructure can rely on the IoT ecosystem Cars are becoming connected and autonomous Moving Forward Why Development Organizations Should Care About Securing IoT Products IoT Device Security Challenges IoT products may be deployed in insecure or physically exposed environments Security is new to many manufacturers and there is limited security planning in development methodologies Security is not a business driver and there is limited security sponsorship and management support in development of IoT products There is a lack of defined standards and reference architecture for secure IoT development There are difficulties recruiting and retaining requisite skills for IoT development teams including architects, secure software engineers, hardware security engineers, and security testing staff The low price point increases the potential adversary pool Resource constraints in embedded systems limit security options IoT Security Survey Guidance for Secure IoT Development 1. Start with a Secure Development Methodology Security Requirements Security Processes Perform Safety Impact Assessment Perform Threat Modeling 2. Implement a Secure Development and Integration Environment Evaluate Programming Languages OWASP Python Security Project Link Integrated Development Environments Continuous Integration Plugins Testing and Code Quality Processes 3. Identify Framework and Platform Security Features Selecting an Integration Framework Evaluate Platform Security Features 4.
    [Show full text]
  • Internet of Things (Iot) Operating Systems Management: Opportunities, Challenges, and Solution
    sensors Editorial Internet of Things (IoT) Operating Systems Management: Opportunities, Challenges, and Solution Yousaf Bin Zikria 1, Sung Won Kim 1,*, Oliver Hahm 2, Muhammad Khalil Afzal 3 and Mohammed Y. Aalsalem 4 1 Department of Information and Communication Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea; [email protected] 2 Zühlke Group, Eschborn, 65760 Hessen, Germany; [email protected] 3 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan; [email protected] 4 Department of Computer Networks, Jazan University, Jazan 45142, Saudi Arabia; [email protected] * Correspondence: [email protected]; Tel.: +82-53-810-4742 Received: 9 April 2019; Accepted: 11 April 2019; Published: 15 April 2019 Abstract: Internet of Things (IoT) is rapidly growing and contributing drastically to improve the quality of life. Immense technological innovations and growth is a key factor in IoT advancements. Readily available low cost IoT hardware is essential for continuous adaptation of IoT. Advancements in IoT Operating System (OS) to support these newly developed IoT hardware along with the recent standards and techniques for all the communication layers are the way forward. The variety of IoT OS availability demands to support interoperability that requires to follow standard set of rules for development and protocol functionalities to support heterogeneous deployment scenarios. IoT requires to be intelligent to self-adapt according to the network conditions. In this paper, we present brief overview of different IoT OSs, supported hardware, and future research directions. Therein, we provide overview of the accepted papers in our Special Issue on IoT OS management: opportunities, challenges, and solution.
    [Show full text]
  • Security and Privacy in the Internet of Things
    Security and Privacy in the Internet of Things Elisa Bertino CS Department and Cyber2SLab [email protected] The IoT – Wikipedia • The Internet of Things (IoT) is the network of physical objects or "things" embedding electronics, software, and network connectivity, which enables these objects to collect and exchange data. • The IoT allows objects to be sensed and controlled remotely across existing network infrastructure, creating opportunities for more Diagram from K. Angrisci “Turning IoT into Internet of direct integration between the physical world Vulnerabilities: IoT Botnets” and computer-based systems. • When IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber- physical systems, which also encompasses technologies such as smart grids, smart homes, intelligent transportation and smart cities. Edge Computing Conventional cloud computing paradigm Edge computing paradigm Diagrams from paper: W. Shi et al, “Edge Computing: Vision and Challenges”, IoT Journal, Oct. 2016 Industrial IoT (IIoT) Diagrams and examples from Accenture “Driving the Unconventional Growth through the Industrial Internet of Things”, 2015, downloaded from https://www.accenture.com/us- en/_acnmedia/Accenture/next-gen/reassembling- industry/pdf/Accenture-Driving-Unconventional-Growth- through-IIoT.pdf Industrial IoT (IIoT) Diagram from BSQUARE. Annual IIOT Maturity Survey. 2017 IoT - Risks IoT dramatically expands the attack surface • IoT systems do not have well defined perimeters • IoT systems are highly dynamic and continuously evolve because of mobility • IoT are highly heterogeneous with respect to: q Communication q Platform q Devices • IoT systems may include physically unprotected portions • IoT systems are highly autonomous and control other autonomous systems • IoT systems may include “objects” not designed to be connected to the Internet • Human interaction with all the devices is not scalable IoT - Risks The OWASP Internet of Things Top 10 - 2014 1.
    [Show full text]