Cognitive Radio-based Internet of Vehicles Architectures, Applications and Open issues

The incorporation of Cognitive Radio (CR) into the Internet of Vehicles (IoV) has emerged as the Intelligent Transportation System (ITS). Section 1 covers the aspects of cognitive radio when it provides support to IoV. The challenges which limit the performance of ITS are highlighted in this chapter. These issues include unreliable delivery, the dynamic topology of IoV, routing overhead, scalability, and energy, to name a few. The issues can be considered as future research directions for a promising intelligent transportation system. Machine learning (ML) is a promising discipline of Artificial Intelligence (AI) to train the CR-based IoV system so that it can make decisions for improved spectrum utilization. The ML-enabled IoV systems can better adapt to the dynamically changing environment. Section 2 covers the applications of ML techniques to the CR-IoV systems and highlights their issues and challenges. Section 3 covers the examination of ML in conjunction with Data Science applications which further widens the scope of the readership. In CR-IoV, ML and Data Science can be collaboratively used to further enhance road safety through inter-vehicle, intra-vehicle, and beyond-vehicle networks.

The channel switching and routing overhead is an important issue in CR-based IoVs. To minimize the channel switching and routing overheads, an effective scheme has been presented in Section 4 to discuss the promising solutions and performance analysis. Meanwhile, IoV communication is a highly time-sensitive application that requires that the vehicles should be synchronized. The time synchronization in IoVs has been highlighted in Section 5 to elaborate further on the critical metrics, challenges, and advancements in synchronization of IoVs. As the vehicles exchange data using wireless channels, they are at risk of being exposed to various security threats. The eavesdropping, identity exposure, message tampering, or sinkhole attack to name a few. It needs time to discuss the security issues and their countermeasures to make the CR-IoV attack resilient. The last section of the book highlights the security issues and maintaining the quality of service (QoS) of the CR-based IoVs which concludes the book.

Key features of the book are mentioned below:

? The architecture and applications of Intelligent Transportation System (ITS) in CR-IoVs.

? The overview of ML techniques and their applications in CR-IoVs.

? The ML applications in conjunction with Data Science in CR-IoVs.

? A minimized channel switching and routing (MCSR) technique to improve the performance of CR-IoVs.

? Data Science applications and approaches to improve the inter and intra-vehicle communications in CR-IoVs.

? The classification of security threats and their countermeasures in CR-IoVs.

? The QoS parameters and their impact on the performance of the CR-IoV ecosystem.

The targeted audience of this book can be undergraduate and graduate-level students, researchers, scientists, academicians, and professionals in the industry. This book will greatly help the readers to understand the application scenarios, the issues and challenges, and the possible solutions. All the chapters highlight the future research directions that can be taken as research topics for future research.

About the Editors. List of Contributors. Section 1: Introduction to CR-IoV. Chapter 1: Intelligent Transportation System in Cognitive Radio Internet of Vehicles. Section 2: Machine Learning in CR-IoV. Chapter 2: Machine Learning Applications in CR-IoV. Chapter 3: Machine Learning Techniques in Conjunction with Data Science Applications for CR-IoV. Section 3: Protocol and Infrastructure. Chapter 4: Minimized Channel Switching and Routing (MCSR) Protocol for Cognitive Radio-based Internet of Vehicles. Chapter 5: Time Synchronization in Cognitive Radio-based Internet of Vehicles. Section 4: Data Science Applications. Chapter 6: Data Science Applications, Approaches, and Challenges in Cognitive Radio-Based IoV Systems. Section 5: Security and Quality of Service. Chapter 7: Security Threats and Countermeasures in Internet of Vehicles (IoV). Chapter 8: QoS Provisioning in CR-IoV

Syed Hashim Raza Bukhari (M’21, SM’21) is an Associate Professor in Department of Electrical & Computer Engineering, Air University, Islamabad, Pakistan. He is the director of Next Generation Intelligent Networks (NGIN) research group. He also served at COMSATS University Islamabad for 9 years. Hashim obtained his Ph. D degree in Electrical Engineering in 2017 from COMSATS University Islamabad (CUI), Wah Campus, Pakistan. Earlier, he received his M.S and B.Eng. degree in Computer Engineering in 2011 and 2007 respectively. He has more than 15 years of experience in academics and has received numerous appreciations upon his contributions for the improvement of teaching standards. He has also received the research productivity award for his research contributions in 2017 from COMSATS. His research interests include the issues in wireless sensor networks with dynamic spectrum access, cognitive radio networks and ad hoc networks. Hashim is currently serving as Editor IEEE Future Direction Newsletter, Guest Editor Journal of Networks and Systems Management (Springer). He is also serving as reviewer of IEEE Communications Surveys and Tutorials, IEEECommunication Magazine, IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, IEEE Communication Letters, Pervasive and Mobile Computing (Elsevier), Computers and Electrical Engineering (Elsevier), IEEE Access, Wireless Networks (Springer), Future Generation Computer Systems (Elsevier), and Ad Hoc Sensor Wireless Networks.

Personal Webpage: https://sites.google.com/view/hashimbukhari

Muhammad Maaz Rehan (M’16, SM’17) is a Tenured Associate Professor in the Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan, and is leading the Intelligent SEcure Networks (IntelliSEN) Research Group. He obtained PhD