Details

Internet Access in Vehicular Networks

von: Wenchao Xu, Haibo Zhou, Xuemin (Sherman) Shen
CHF 142.00
Verlag:	Springer
Format:	PDF
Veröffentl.:	18.11.2021
ISBN/EAN:	9783030889913
Sprache:	englisch
Anzahl Seiten:	165

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Beschreibungen

Titelbeschreibung

This book introduces the Internet access for vehicles as well as novel communication and computing paradigms based on the Internet of vehicles. To enable efficient and reliable Internet connection for mobile vehicle users, this book first introduces analytical modelling methods for the practical vehicle-to-roadside (V2R) Internet access procedure, and employ the interworking of V2R and vehicle-to-vehicle (V2V) to improve the network performance for a variety of automotive applications. In addition, the wireless link performance between a vehicle and an Internet access station is investigated, and a machine learning based algorithm is proposed to improve the link throughout by selecting an efficient modulation and coding scheme.This book also investigates the distributed machine learning algorithms over the Internet access of vehicles. A novel broadcasting scheme is designed to intelligently adjust the training users that are involved in the iteration rounds for an asynchronous federated learning scheme, which is shown to greatly improve the training efficiency. This book conducts the fully asynchronous machine learning evaluations among vehicle users that can utilize the opportunistic V2R communication to train machine learning models. Researchers and advanced-level students who focus on vehicular networks, industrial entities for internet of vehicles providers, government agencies target on transportation system and road management will find this book useful as reference. Network device manufacturers and network operators will also want to purchase this book.

Inhaltsverzeichnis

Overview of Internet Access of  Vehicular Networks.- Internet Access Modeling of  Vehicular Internet Access.- V2X Interworking via Vehicular Internet Access.- Intelligent Link Management for Vehicular Internet Access.- Intelligent Networking enabled Vehicular Distributed Learning.- Conclusion and Future Works.

Autorenportrait

Wenchao Xu (M’18) received the B.E. and M.E. degrees from Zhejiang University, Hangzhou, China, in 2008 and 2011, respectively. He received the Ph.D. degree with the Department of Electrical and Computer Engineering, University of Waterloo in 2018. In 2011, he joined Alcatel Lucent Shanghai Bell Co. Ltd., where he was a Software Engineer for telecom virtualization. Currently he is a research assistant professor at Department of Computing, The Hong Kong Polytechnic University. His research interests include wireless communications with emphasis on resource allocation, AI enable Internet of things, and edge computing. He is a member of IEEE.  Haibo Zhou (M’14—SM’18) received the Ph.D. degree in information and communication engineering from Shanghai Jiao Tong University, Shanghai, China, in 2014. From 2014 to 2017, he worked as a Postdoctoral Fellow with the Broadband Communications Research Group, Electrical and Computer Engineering Department, University of Waterloo. He is currently an Associate Professor with the School of Electronic Science and Engineering, Nanjing University, Nanjing, China. His research interests include resource management and protocol design in cognitive radio networks and vehicular networks.Xuemin (Sherman) Shen (M’97—SM’02—F’09) received the Ph.D. degree in electrical engineering from Rutgers University, New Brunswick, NJ, USA, in 1990. He is currently a University Professor with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. His research focuses on resource management in interconnected wireless/wired networks, wireless network security, social networks, smart grid, and vehicular ad hoc and sensor networks. He is a registered Professional Engineer of Ontario, Canada, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, a Royal Society of Canada Fellow, and a Distinguished Lecturer of the IEEE Vehicular Technology Society and Communications Society. Prof. Shen received the R.A. Fessenden Award in 2019 from IEEE, Canada, the James Evans Avant Garde Award in 2018 from the IEEE Vehicular Technology Society, the Joseph LoCicero Award in 2015 and the Education Award in 2017 from the IEEE Communications Society. He has also received the Excellent Graduate Supervision Award in 2006 and the Outstanding Performance Award 5 times from the University of Waterloo and the Premier’s Research Excellence Award (PREA) in 2003 from the Province of Ontario, Canada. He served as the Technical Program Committee Chair/Co-Chair for the IEEE Globecom’16, the IEEE Infocom’14, the IEEE VTC’10 Fall, the IEEE Globecom’07, the Symposia Chair for the IEEE ICC’10, the Tutorial Chair for the IEEE VTC’11 Spring, the Chair for the IEEE Communications Society Technical Committee on Wireless Communications, and P2P Communications and Networking. He is the Editor-in-Chief of the IEEE INTERNET OF THINGS JOURNAL and the Vice President on Publications of the IEEE Communications Society.

Back cover copy

This book introduces the Internet access for vehicles as well as novel communication and computing paradigms based on the Internet of vehicles. To enable efficient and reliable Internet connection for mobile vehicle users, this book first introduces analytical modelling methods for the practical vehicle-to-roadside (V2R) Internet access procedure, and employ the interworking of V2R and vehicle-to-vehicle (V2V) to improve the network performance for a variety of automotive applications. In addition, the wireless link performance between a vehicle and an Internet access station is investigated, and a machine learning based algorithm is proposed to improve the link throughout by selecting an efficient modulation and coding scheme.This book also investigates the distributed machine learning algorithms over the Internet access of vehicles. A novel broadcasting scheme is designed to intelligently adjust the training users that are involved in the iteration rounds for an asynchronous federated learning scheme, which is shown to greatly improve the training efficiency. This book conducts the fully asynchronous machine learning evaluations among vehicle users that can utilize the opportunistic V2R communication to train machine learning models. Researchers and advanced-level students who focus on vehicular networks, industrial entities for internet of vehicles providers, government agencies target on transportation system and road management will find this book useful as reference. Network device manufacturers and network operators will also want to purchase this book.

Sonderbeitrag

Describes the actual Internet connection for vehicle users from the perspective of V2V and V2R link management Considers and discusses the asynchronous machine learning training in vehicular environments Provides inspirations on the Internet access mechanism design as well as applying the machine learning paradigms