This book gathers recent research works in emerging Artificial Intelligence (AI) methods for the convergence of communication, caching, control, and computing resources in cloud-based Internet of Vehicles (IoV) infrastructures. In this context, the book's major subjects cover the analysis and the development of AI-powered mechanisms in future IoV applications and architectures. It addresses the major new technological developments in the field and reflects current research trends and industry needs. It comprises a good balance between theoretical and practical issues, covering case studies, experience and evaluation reports, and best practices in utilizing AI applications in IoV networks. It also provides technical/scientific information about various aspects of AI technologies, ranging from basic concepts to research-grade material, including future directions. This book is intended for researchers, practitioners, engineers, and scientists involved in designing and developing protocols and AI applications and services for IoV-related devices.

Autorentext

Dr. Naercio Magaia received his (five-year) B.Sc. degree in Electrical Engineering from Eduardo Mondlane University in 2006, his M.Sc. in Communication Networks Engineering from Instituto Superior Tecnico, University of Lisbon (IST/ULisboa) in 2011, and his Ph.D. in Electrical and Computer Engineering from IST/ULisboa in 2017. He currently serves as an Invited Assistant Professor in the Department of Informatics at the Faculty of Sciences of the University of Lisbon. He has actively participated in national and EU-funded research projects. He has more than 25 publications at various international conferences, workshops, scientific journals, and book chapters. Dr. Magaia has acted as a reviewer for several international journals and conferences and has a member of technical programs and organizing committees. His research interests include Computer Networks, the Internet of things, Cybersecurity, and Artificial Intelligence. 

Dr. George Mastorakis received his B.Eng. (Honours) in Electronic Engineering from UMIST (University of Manchester Institute of Science & Technology) in 2000, his M.Sc. in Telecommunications from UCL (University College London) in 2001 and his Ph.D. in Telecommunications from University of the Aegean in 2008. He currently serves as an Associate Professor in the Department of Management Science and Technology at Hellenic Mediterranean University in Greece and as a Director of e-Business Intelligence Laboratory. He has actively participated in a large number of EU funded research projects (FP6, FP7 and Horizon2020) and national research ones. He has also acted as a technical manager in many research projects funded by GSRT (General Secretariat for Research & Technology, Ministry of Development, Greece). He has more than 300 publications at various international conferences proceedings, workshops, scientific journals and book chapters. He has acted as a reviewer for several scientific journals and member of conferences technical program committees. He is also author/editor of more than ten books. His research interests include cognitive radio networks, IoT applications, IoE architectures, radio resource management, Artificial Intelligence applications, Internet of Vehicles technologies and 5G mobile networks.

  Dr. Constandinos Mavromoustakis is currently a Professor at the Department of Computer Science at the University of Nicosia, Cyprus. He received a five-year dipl.Eng in Electronic and Computer Engineering from Technical University of Crete, Greece, MSc in Telecommunications from University College of London, UK, and his Ph.D. from the department of Informatics at Aristotle University of Thessaloniki, Greece. Dr. Mavromoustakis is leading the Mobile Systems Lab. (MOSys Lab., http://www.mosys.unic.ac.cy/) at the Department of Computer Science at the University of Nicosia, dealing with design and implementation of hybrid wireless testbed environments, high performance cloud and mobile cloud computing (MCC) systems, IoT/IoE, modeling and simulation of mobile computing environments and protocol development and deployment for large-scale heterogeneous networks as well as new 'green' mobility-based protocols. He has worked as a Research Associate and Postdoctoral Researcher at the Computer Architecture and Communications Lab at the PDSG (Parallel and Distributed Systems Group) at the Department of Informatics, Aristotle University of Thessaloniki, Greece. He has also worked as a Visiting faculty/Special Expert Scientist (2009-2010) at the Cyprus University of Technology at the Department of Electrical Engineering and Information Technologies (EEIT) and he has been appointed for a semester as a visiting Assistant Professor (2011) at the Coventry University, at the Faculty of Engineering, at the Department of Computing and the Digital Environment, UK. He has authored/edited four Books to-date, whereas he is a management member of IEEE Communications Society (ComSoc) Radio Communications Committee (RCC) and served as track Chair and co-Chair of various IEEE International Conferences (including AINA, IWCMC, IEEE Internet of Things etc). He is the recipient of various grants including the highly competitive European grant of Early Stage Researcher (ESR), for the excellent research output and research impact, in December 2013 (EU secretariat/Brussels).

Dr. Evangelos Pallis is a Professor in the Department of Electrical and Computer Engineering at Hellenic Mediterranean University and co-director of Research and Development of Telecommunication Systems Laboratory PASIPHAE of the same Department. He received his B.Sc. in Electronic Engineering from the Technological Educational Institute of Crete in 1994, his M.Sc. in Telecommunications from University of East London, in 1997, and received his Ph.D. in Telecommunications from the University of East London in ...

Inhalt
Introduction.- Introduction of AI and IoV.- Architectures and systems for AI and IoV convergence.- IoV with Machine Learning and Artificial Immune System technologies.- AI and IoV applications.- AI enabled IoV systems.- Performance Evaluation of Deep Learning and IoV-related mechanisms.- Conclusion.