The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

https://ir.nctu.edu.tw:443/bitstream/11536/145531/1/3ecec2a2f6e38b0824ccca63f66259e9.pdf

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

Safety-critical applications in cooperative vehicular networks require authentication of nodes and messages. Yet, privacy of individual vehicles and drivers must be maintained. Pseudonymity can satisfy both security and privacy requirements. Thus, a large body of work emerged in recent years, proposing pseudonym solutions tailored to vehicular networks. In this survey, we detail the challenges and requirements for such pseudonym mechanisms, propose an abstract pseudonym lifecycle, and give an extensive overview and categorization of the state of the art in this research area. Specifically, this survey covers pseudonym schemes based on public key and identity-based cryptography, group signatures and symmetric authentication. We compare the different approaches, give an overview of the current state of standardization, and identify open research challenges.

Pseudonym Schemes in Vehicular Networks: A Survey

Throughout the last century, the automobile industry achieved remarkable milestones in manufacturing reliable, safe, and affordable vehicles. Because of significant recent advances in computation and communication technologies, autonomous cars are becoming a reality. Already autonomous car prototype models have covered millions of miles in test driving. Leading technical companies and car manufacturers have invested a staggering amount of resources in autonomous car technology, as they prepare for autonomous cars’ full commercialization in the coming years. However, to achieve this goal, several technical and nontechnical issues remain: software complexity, real-time data analytics, and testing and verification are among the greater technical challenges; and consumer stimulation, insurance management, and ethical/moral concerns rank high among the nontechnical issues. Tackling these challenges requires thoughtful solutions that satisfy consumers, industry, and governmental requirements, regulations, and policies. Thus, here we present a comprehensive review of state-of-the-art results for autonomous car technology. We discuss current issues that hinder autonomous cars’ development and deployment on a large scale. We also highlight autonomous car applications that will benefit consumers and many other sectors. Finally, to enable cost-effective, safe, and efficient autonomous cars, we discuss several challenges that must be addressed (and provide helpful suggestions for adoption) by designers, implementers, policymakers, regulatory organizations, and car manufacturers.

Autonomous Cars: Research Results, Issues, and Future Challenges

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

We present a realistic, yet computationally inexpensive simulation model for IEEE 802.11p radio shadowing in urban environments. Based on real world measurements using IEEE 802.11p/DSRC devices, we estimated the effect that buildings and other obstacles have on the radio communication between vehicles. Especially for evaluating safety applications in the field of Vehicular Ad Hoc Networks (VANETs), stochastic models are not sufficient for evaluating the radio communication in simulation. Motivated by similar work on WiFi measurements, we therefore created an empirical model for modeling buildings and their properties to accurately simulate the signal propagation. We validated our model using real world measurements in a city scenario for different types of obstacles. Our simulation results show a very high accuracy when compared with the measurement results, while only requiring a marginal overhead in terms of computational complexity.

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A computationally inexpensive empirical model of IEEE 802.11p radio shadowing in urban environments

The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature make an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey, we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over 80 privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.

Technical Privacy Metrics: A Systematic Survey

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers, and data sources for the attacks, giving structure to the fuzzy term “smart city.” Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities.

Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions

We describe Veins, an open-source model library for (and a toolbox around) OMNeT++, which supports researchers conducting simulations involving communicating road vehicles—either as the main focus of a study or as a component. Veins already includes a full stack of simulation models for investigating cars and infrastructure communicating via IEEE 802.11 based technologies in simulations of Vehicular Ad Hoc Networks (VANETs) and Intelligent Transportation Systems (ITS). Thanks to its modularity, though, it can equally well be used as the basis for modeling other mobile nodes (like bikes or pedestrians) and communication technologies (from mobile broadband to visible light). Serving as the basis for hundreds of publications and university courses since its beginnings in the year 2006, today Veins is both one of the most mature and established tools in this domain.

Veins: The Open Source Vehicular Network Simulation Framework

We study the effectiveness of Inter-Vehicle Communication (IVC) in urban and suburban environments at low node densities, with a particular focus on cooperative awareness and traffic safety. The recently standardized DSRC/WAVE protocol suite defines a platform for such applications, which mainly focus on beaconing, i.e., periodic 1-hop-broadcast. In general, such safety relevant transmissions are defined by time criticality. One of the major problems to be solved is how to tackle the very difficult and complex radio signal attenuation due to buildings and other obstacles, especially in cities. Typical concepts address this problem by requiring all vehicles to also act as relays or by using dedicated Roadside Units (RSUs). We show how such systems may be operated more efficiently and how the situation can be further improved by relying on parked vehicles in addition to, or as a replacement for, RSUs. Given the fact that the U.S. DOT is already evaluating whether to make DSRC mandatory for new cars, wide availability of radio equipped cars can be predicted; also the impact in terms of energy consumption is negligible. We performed an extensive set of simulations to evaluate the negative impact of buildings at low node densities and the benefit of our proposal. Our results clearly indicate that situation awareness can be significantly improved. When disseminating safety critical events in a realistic scenario, reasonable numbers of parked cars can increase cooperative awareness by up to 25%, a factor which requires an unreasonably costly number of RSUs. To the best of our knowledge, we are the first to propose the utilization of parked vehicles as relay nodes for safety applications in vehicular networks.

David Eckhoff

Papers

A computationally inexpensive empirical model of IEEE 802.11p radio shadowing in urban environments

Technical Privacy Metrics: A Systematic Survey

Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions

Veins: The Open Source Vehicular Network Simulation Framework

IVC in Cities: Signal Attenuation by Buildings and How Parked Cars Can Improve the Situation