Intelligent Resource Management in Vehicular Networks

ISBN-13: 9783030965068 / Angielski / Twarda / 2022

Haixia Peng;Qiang Ye; Xuemin Sherman Shen

Opis

This book provides a comprehensive investigation on new technologies for future vehicular networks. The authors propose different schemes to efficiently manage the multi-dimensional resources for supporting diversified applications. The authors answer the questions of why connected and automated vehicle technology should be considered; how the multi-access edge computing (MEC) and unmanned aerial vehicle (UAV) technologies can be helpful to vehicular networks; how to efficiently manage the multi-dimensional resources to support different vehicular applications with guaranteed quality-of-service (QoS) requirements; and how to adopt optimization and AI technologies to achieve resource management in vehicular networks. The book is pertinent to researchers, professionals, academics and students in vehicular technologies.

Spis treści

Introduction.- Overview of Vehicular Networks.- Resource Management in Vehicular Networks.- MEC-Assisted Vehicular Networks.- Spectrum Resource Management in MEC-Assisted ADVNETs.- Multi-Dimensional Resource Management in MVNETs.- Multi-Dimensional Resource Management in UAV-Assisted MVNETs.- Conclusion.

O autorze

Haixia Peng (Member, IEEE) received her M.S., first Ph.D., and second Ph.D. degrees in Electronics and Communication Engineering, Computer Science, and Electrical and Computer Engineering from Northeastern University, Shenyang, China, in 2013 and 2017, and University of Waterloo, Waterloo, Canada in 2021, respectively. She is now an assistant professor with the Department of Computer Engineering and Computer Science, California State University Long Beach, Long Beach, CA, USA. Her current research focuses on satellite-terrestrial vehicular networks, multi-access edge computing, resource management, and reinforcement learning. She served as a Technical Program Committee (TPC)  member in IEEE VTC-fall 2016\&2017, IEEE ICCEREC 2018, IEEE Globecom 2016-2021, and IEEE ICC 2017-2022 conferences. 

Qiang Ye (Member, IEEE) received the Ph.D. degree in electrical and computer engineering from the University of Waterloo, ON, Canada, in 2016. Since Sept. 2021, he has been an Assistant Professor with the Department of Computer Science, Memorial University of Newfoundland, NL, Canada. Before joining Memorial, he had been with the Department of Electrical and Computer Engineering and Technology, Minnesota State University, Mankato, USA, as an Assistant Professor from Sept. 2019 to Aug. 2021 and with the Department of Electrical and Computer Engineering, University of Waterloo as a Postdoctoral Fellow and Research Associate from Dec. 2016 to Sept. 2019. He has published over 45 research articles on top ranked IEEE Journals and Conference Proceedings. He is/was TPC co-chairs/members for several international conferences and workshops, including the IEEE GLOBECOM’20, VTC’17, VTC’20, ICPADS’20, and INFOCOM’22. He serves as associate editors for IEEE Open Journal of the Communications Society, Peer-to-Peer Networking and Applications, International Journal of Distributed Sensor Networks, and Wireless Networks. 

Xuemin Sherman Shen (Fellow, IEEE) 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, Canada. His research focuses on network resource management, wireless network security, Internet of Things, 5G and beyond, 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, a Chinese Academy of Engineering Foreign Fellow, and a Distinguished Lecturer of the IEEE Vehicular Technology Society and Communications Society. Dr. Shen received the 2021 Canadian Award for Telecommunications Research, the R.A. Fessenden Award in 2019 from IEEE, Canada, James Evans Avant Garde Award in 2018 from the IEEE Vehicular Technology Society, and Joseph LoCicero Award in 2015 and Education Award in 2017 from the IEEE Communications Society. He has also received the Excellent Graduate Supervision Award in 2006 and 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, and the Chair for the IEEE Communications Society Technical Committee on Wireless Communications. Dr. Shen is the elected IEEE Communications Society Vice President for Technical and Educational Activities, Vice President for Publications, Member-at-Large on the Board of Governors, Chair of the Distinguished Lecturer Selection Committee, Member of IEEE Fellow Selection Committee. He was/is the Editor-in-Chief of the IEEE INTERNET OF THINGS JOURNAL, IEEE Network, IET Communications, and Peer-to-Peer Networking and Applications.

Blurb

This book provides a comprehensive investigation on new technologies for future vehicular networks. The authors propose different schemes to efficiently manage the multi-dimensional resources for supporting diversified applications. The authors answer the questions of why connected and automated vehicle technology should be considered; how the multi-access edge computing (MEC) and unmanned aerial vehicle (UAV) technologies can be helpful to vehicular networks; how to efficiently manage the multi-dimensional resources to support different vehicular applications with guaranteed quality-of-service (QoS) requirements; and how to adopt optimization and AI technologies to achieve resource management in vehicular networks. The book is pertinent to researchers, professionals, academics and students in vehicular technologies.

• Provides a comprehensive resource management design for vehicular networks;
• Includes multi-resource management studies in highly dynamic wireless networks;
• Features applications of AI technologies in multi-dimensional resource management.