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
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.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Postgraduate and Professional
Illustrationen
6 s/w Photographien bzw. Rasterbilder, 63 s/w Zeichnungen, 14 s/w Tabellen, 69 s/w Abbildungen
14 Tables, black and white; 63 Line drawings, black and white; 6 Halftones, black and white; 69 Illustrations, black and white
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 16 mm
Gewicht
ISBN-13
978-1-032-25755-6 (9781032257556)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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, IEEE Communication 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 from Universiti Teknologi PETRONAS (UTP), Malaysia in Jan 2016 with two Bronze medals. He is twice an Internet Society (ISOC) fellow for the IETF 82nd and 87th; and ISOC fellow for Global INET 2012; an Editor of IEEE Softwarization Newsletter; and an Associate Editor of IEEE Access journal. He is the Senior Lead in the Erasmus+ SAFE-RH Project, Sensing, ArtiFicial intelligence, and Edge networking towards Rural Health monitoring (project no. 619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP). Dr. Maaz is the Lead Editor of the book, "Blockchain-enabled Fog and Edge Computing: Concepts, Architectures and Applications", published by Taylor & Francis Group, CRC Press, USA. The research areas of Dr. Maaz include Blockchain & Smart Contracts, Internet of Everything (IoT/IoV), Cybersecurity, and AI-based Networks. Dr. Maaz has been involved in curriculum design and development activities and possesses a blend of extensive teaching experience at the National and International levels.
Personal Webpage: https://sites.google.com/view/maazrehan/
Mubashir Husain Rehmani (M'14-SM'15, SFHEA) received the B.Eng. degree in computer systems engineering from Mehran University of Engineering and Technology, Jamshoro, Pakistan, in 2004, the M.S. degree from the University of Paris XI, Paris, France, in 2008, and the Ph.D. degree from the University Pierre and Marie Curie, Paris, in 2011. He is currently working as Lecturer in the Department of Computer Science, Munster Technological University (MTU), Ireland. Prior to this, he worked as Post Doctoral Researcher at the Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology (WIT), Waterford, Ireland. He also served for five years as an Assistant Professor at COMSATS Institute of Information Technology, Wah Cantt., Pakistan. He is serving as an Editorial Board Member of NATURE Scientific Reports. He is currently an Area Editor of the IEEE Communications Surveys and Tutorials. He served for three years (from 2015 to 2017) as an Associate Editor of the IEEE Communications Surveys and Tutorials. He served as Column Editor for Book Reviews in IEEE Communications Magazine. He is appointed as Associate Editor for IEEE Transactions on Green Communication and Networking. Currently, he serves as Associate Editor of IEEE Communications Magazine, Elsevier Journal of Network and Computer Applications (JNCA), and the Journal of Communications and Networks (JCN). He is also serving as a Guest Editor of Elsevier Ad Hoc Networks journal, Elsevier Future Generation Computer Systems journal, the IEEE Transactions on Industrial Informatics, and Elsevier Pervasive and Mobile Computing journal. He has authored/edited total eight books. Two books with Springer, two books published by IGI Global, USA, three books published by CRC Press - Taylor and Francis Group, UK, and one book with Wiley, U.K. He received "Best Researcher of the Year 2015 of COMSATS Wah" award in 2015. He received the certificate of appreciation, "Exemplary Editor of the IEEE Communications Surveys and Tutorials for the year 2015" from the IEEE Communications Society. He received Best Paper Award from IEEE ComSoc Technical Committee on Communications Systems Integration and Modeling (CSIM), in IEEE ICC 2017. He consecutively received research productivity award in 2016-17 and also ranked # 1 in all Engineering disciplines from Pakistan Council for Science and Technology (PCST), Government of Pakistan. He received Best Paper Award in 2017 from Higher Education Commission (HEC), Government of Pakistan. He is the recipient of Best Paper Award in 2018 from Elsevier Journal of Network and Computer Applications. He is the recipient of Highly Cited Researcher (TM) award thrice in 2020, 2021, and 2022 by Clarivate, USA. His performance in this context features in the TOP 1% by citations in the field of Computer Science and Cross Field in the Web of Science (TM) citation index. He is the ONLY Researcher from Ireland in the field of "Computer Science" who received this International prestigious award. In Oct 2022, he received Science Foundation Ireland's CONNECT Centre's Education and Public Engagement (EPE) Award 2022 for his research outreach work and being a spokesperson for achieving a work-life balance for a career in research.
Personal Webpage: https://sites.google.com/site/mubrehmani/
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
