DOCSLIB.ORG

Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues Khondokar Fida Hasan * Tarandeep Kaur Md. Mhedi Hasan Yanming Feng School of EECS, QUT School of EECS, QUT Dept. of ICT, CoU School of EECS, QUT Brisbane, Australia Brisbane, Australia Cumilla, Bangladesh Brisbane, Australia Abstract—Over the past few years, we have experienced great and traffic management system was envisioned since the year technological advancements in the information and communi- 1990 by combining different sensors, and different mode cation field, which has significantly contributed to reshaping of applications under the technological evolution of Intelli- the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect gent Transportation System (ITS). At its earlier stage, this data, the data exchanged paradigm among vehicles is shifted technological endeavour evolved with the integration of co- from the local network to the cloud. With the introduction of employable and assisting correspondence innovation termed as cloud and edge computing along with ubiquitous 5G mobile Co-operative Intelligent Transportation System (CITS), which network, it is expected to see the role of Artificial Intelligence basically incorporates the Information and Communication (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge Technologies (ICT) with transportation infrastructure [5]. The of industrial revolution 4.0, it is necessary first of all to get a vehicle is enabled to communicate with other vehicles, road- clear understanding of the cutting-edge technologies that going to side infrastructures and other road entities by creating vehic- take place in the automotive ecosystem so that the cyber-physical ular ad-hoc networks (VANET). However, both of these two impact on transportation system can be measured. CIoV, which technological advancements lead the concept of Autonomous is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in and Connected vehicular technologies in parallel to enrich the transportation, and it has amassed great attention. It introduces idea of intelligence in the transport sector so as to increase cloud-based artificial intelligence and machine learning into the user comfort and road safety. transportation system. What are the future expectations of CIoV? With the evolution of the Internet of Things (IoT), in the To fully contemplate this architectures future potentials, and early 2010s, meanwhile, vehicles are being connected to the milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet Internet, aiming at providing ubiquitous access to information the security requirements of its practical implementation. Aiming alike to the drivers and passengers. This leads to another to that, this paper presents the evolution of CIoV along with the technological break-through termed as Internet of Vehicle layer abstractions to outline the distinctive functional parts of (IoV). the proposed architecture. It also gives an investigation of the Even though there has been a noticeable advancement in prime security and privacy issues associated with technological evolution to take measures. terms of automation and connectivity, still, it is not sufficient Index Terms—Cognitive Internet of Vehicles, Automotive, to reduce the road causalities to zero. Driving errors, as well as Transportation, Industrial Revolution 4.0, Security, Intelligent the drivers misjudgement being the prime reasons associated Transportation System with this causalities where a recent research [6] reveals that 90% of road accidents are caused by human factors. Take, for I. INTRODUCTION instance, fatigue while driving, overspreading, blocked line of The transport sector plays a key role in modern civilisation, sight, etc., are ranked among the most common factors that arXiv:1912.03356v1 [cs.NI] 20 Nov 2019 and over the past years, it has experienced rapid growth. cause accidents. This encourages the necessity of applying Ma- According to a recent survey; 2019s Motor Vehicle Census, chine Learning (ML), Neural Network (NN), Deep Learning there is an annual increase rate of 1.7% (average) in Australia, (DL), and Artificial Intelligence (AI) that can take control of with 19.5 million registered motor vehicles over a population wheel which can enable error-free driving, resulting to the idea of 25 million people [1]. This number for the USA and of Cognitive Internet of Vehicles (CIoV) shown in Fig.1. the UK stands at 281.3 million [2] and 39.4 million [3] The advancement that the CIoV offers toward the use of respectively. In turns, the whole world has over 1.4 billion internet and machine intelligence are also associated with new registered vehicles and come the year 2040, this figure is security risks and privacy issues, realising the transportation expected to shoot by double [4]. The critical issue is, as the need to address properly. Since different technologies are number of vehicles on roads increases, traffic-related problems playing a role in different layers, it is vital to understand such as traffic congestion, accident, and road fatalities are the existing vulnerabilities in the generic domain of those on the rise as well. To counter-attack this, a smart transport technologies and their application. This paper aims at giving an overview of the evolution of *Corresponding Author Email: k.fi[email protected] Cognitive Internet of Vehicles (CIoV) and its technological ITS systems. Mostly, all the Automated vehicles are equipped Smart Transport and IOV with a number of sensors, cameras, Lidar, Radar, etc., that Traffic Management System Connected to the Cloud collects raw data from the external environment. This data Combining Different Sensors in the form of Internet of Things and modes of applications then serves as the input to the sophisticated system software Cognitive IoV which is used in vehicles to decide for specific courses of Inception of Autonomous Car actions, such as, lane changing, acceleration and overtaking Internet of Vehicles other vehicles [9]. In connected vehicular technology, however, C-IOV Cooperative Intelligent Cloud-based Advanced Transportation the vehicles communicate with internal and external environ- Transportation System Solution using ML, AI, etc. ments utilising a different kind of communication technologies Intelligent Inception of LTE network predominantly wireless communication technologies. These Transportation System C-ITS Inception of VANET vehicles use wireless networks to create interactions within the Connected Vehicular Technology added with Evolution of Transportation System Transportation of Evolution devices built in the vehicle itself that is On-Board sensors and Wireless Networking e.g. VANET outside the vehicle; that is Vehicle to Vehicle (V2V) commu- Year 1990 2000 2010 2020 nications or Vehicle to Infrastructure (V2I) communications [10]. The concept of connected technologies fundamentally Fig. 1. Evolution of Cognitive Internet of Vehicles (CIoV). propelled the evolution of the transportation system to form the Internet of Vehicles (IoV), thus creating an opportunity to apply modern technological developments to apply on data related reviews. It presents a five-layer model to envisage the such as machine learning and artificial intelligence, to creates architecture of future transportation system to identify their insights on transportation management and to take measures distinctive functional parts. This paper also discusses security on providing better services. risks, including different threats, attacks, and vulnerabilities may associate with different layers to understand the measures B. Network Communication and Data Acquisition required. Layer-2 in CIoV is responsible for network-based com- II. COGNITIVE INTERNET OF VEHICLES (CIOV) AND munications among different transportation entities aiming LAYERED ARCHITECTURE at the transport-related data acquisition. A wide variety of With the advent of the ever-growing vehicular applications, communication interaction that takes place are defined in the technical challenges are growing too to meet the de- this layer. Broadly, all interaction can be classified as Intra- mands from both communication and computation. Without vehicular communication and Inter-vehicular communications. persuasive communication and computational support, a good Communication that takes place within the vehicle is termed number of foreseeing vehicular applications and services will as Intra-vehicular communication. Generally, smart vehicles only still stay in the idea phase and cannot be seen into are equipped with numerous sensors, such as sensors detect practice. the road condition, drivers fatigue, monitoring of the tire pressure, and autonomous control sensors, etc. [10]. The A. Sensing and Participation primary objectives of those sensors in vehicles are to monitor The Layer-1 of the structure represents all the techno- the internal operation of vehicle. Those sensors communicate logically evolved entities that are capable of sensing and with each other and take intelligent decision for the human communicating and also responsible for interacting within the driver. Smart vehicles use technologies that allow them to transportation system. Such entity includes smart vehicles and make decisions for the driver.
Recommended publications
  • Internet of Things Meets Brain-Computer Interface: a Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity

    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.
  • Vision-Based Positioning for Internet-Of-Vehicles Kuan-Wen Chen, Chun-Hsin Wang, Xiao Wei, Qiao Liang, Chu-Song Chen, Ming-Hsuan Yang, and Yi-Ping Hung

    Vision-Based Positioning for Internet-Of-Vehicles Kuan-Wen Chen, Chun-Hsin Wang, Xiao Wei, Qiao Liang, Chu-Song Chen, Ming-Hsuan Yang, and Yi-Ping Hung

    http://ieeexplore.ieee.org/Xplore Vision-Based Positioning for Internet-of-Vehicles Kuan-Wen Chen, Chun-Hsin Wang, Xiao Wei, Qiao Liang, Chu-Song Chen, Ming-Hsuan Yang, and Yi-Ping Hung Abstract—This paper presents an algorithm for ego-positioning Structure by using a low-cost monocular camera for systems based on from moon the Internet-of-Vehicles (IoV). To reduce the computational and For local model memory requirements, as well as the communication load, we construc,on tackle the model compression task as a weighted k-cover problem for better preserving the critical structures. For real-world vision-based positioning applications, we consider the issue of large scene changes and introduce a model update algorithm to For image collec,on address this problem. A large positioning dataset containing data collected for more than a month, 106 sessions, and 14,275 images is constructed. Extensive experimental results show that sub- meter accuracy can be achieved by the proposed ego-positioning algorithm, which outperforms existing vision-based approaches. (a) Index Terms—Ego-positioning, model compression, model up- date, long-term positioning dataset. Download local 3D Upload newly scene model acquired images for model update I. INTRODUCTION NTELLIGENT transportation systems have been exten- I sively studied in the last decade to provide innovative and proactive services for traffic management and driving safety issues. Recent advances in driving assistance systems mostly For image matching provide stand-alone solutions to these issues by using sensors and posi1oning limited to the line of sight. However, many vehicle accidents occur because of other vehicles or objects obstructing the (b) view of the driver.
  • Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril

    Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril

    Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions.
  • Visual Prosthetics Wwwwwwwwwwwww Gislin Dagnelie Editor

    Visual Prosthetics Wwwwwwwwwwwww Gislin Dagnelie Editor

    Visual Prosthetics wwwwwwwwwwwww Gislin Dagnelie Editor Visual Prosthetics Physiology, Bioengineering, Rehabilitation Editor Gislin Dagnelie Lions Vision Research & Rehabilitation Center Johns Hopkins University School of Medicine 550 N. Broadway, 6th floor Baltimore, MD 21205-2020 USA [email protected] ISBN 978-1-4419-0753-0 e-ISBN 978-1-4419-0754-7 DOI 10.1007/978-1-4419-0754-7 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011921400 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface Visual Prosthetics as a Multidisciplinary Challenge This is a book about the quest to realize a dream: the dream of restoring sight to the blind. A dream that may have been with humanity much longer than the idea that disabilities can be treated through technology – which itself is probably a very old idea.
  • The Internet of Things (Iot): an Overview

    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.
  • Handheld Computing Terminal Industrializing Mobile Gateway for the Internet of Things

    Handheld Computing Terminal Industrializing Mobile Gateway for the Internet of Things

    Handheld Computing Terminal Industrializing Mobile Gateway for the Internet of Things Retail Inventory Logistics Management Internet of Vehicles Electric Utility Inspection www.AMobile-solutions.com About AMobile Mobile Gateway for IIoT Dedicated IIoT, AMobile develops mobile devices and gateways for data collectivity, wireless communications, local computing and processing, and connectivity between the fields and cloud. In addition, AMobile's unified management platform-Node-Watch monitors and manages not only mobile devices but also IoT equipment in real-time to realize industrial IoT applications. Manufacturing • IPC mobile • Mobility computing technology Retail Inspection (IP-6X, Military • Premier member grade, Wide- - early access Blockchain temperature) Healthcare Logistics • Manufacturing resource pool Big Data & AI • Logistics • Global customer support Handheld Mobile Computing Data Collector Smart Meter Mobile Inspection Device with Keyboard Assistant Mobile Computing IoT Terminal Device Ultra-rugged Intelligent Barcode Reader Vehicle Mount Handheld Device Handheld Device Tablet Mobile Gateway AMobile Intelligent established in 2013, a joint venture by Arbor Technology, MediaTek, and Wistron, is Pressure Image an expert in industrial mobile computers and solutions for industrial applications including warehousing, Temperature Velocity Gyroscope logistics, manufacturing, and retail. Leveraging group resources of manufacturing and services from Wistron, IPC know-how and distribution channels from Arbor, mobility technology and
  • Fog Computing: a Platform for Internet of Things and Analytics

    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.
  • The Internet of Things: Impact on Public Safety Communications

    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.,
  • Home Automation System Using Google Assistant

    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.
  • A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

    A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

    sensors Review A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges M. Nadeem Ahangar 1, Qasim Z. Ahmed 1,*, Fahd A. Khan 2 and Maryam Hafeez 1 1 School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK; [email protected] (M.N.A.); [email protected] (M.H.) 2 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan; [email protected] * Correspondence: [email protected] Abstract: The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatali- ties in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). There- fore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication tech- nologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium- range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency.
  • 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

    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.
  • Paving the Way for Self-Driving Vehicles”

    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.