Mushu Li, Jie Gao, Xuemin Shen (Sherman), Lian Zhao

eBook, PDF

Intelligent Computing and Communication for the Internet of Vehicles (eBook, PDF)

Beschreibung

This book investigates intelligent network resource management for IoV, with the objective of maximizing the communication and computing performance of vehicle users. Focusing on two representative use cases in IoV, i.e., safety message broadcast and autonomous driving, the authors propose link-layer protocol design and application-layer computing task scheduling to achieve the objective given the unique characteristics and requirements of IoV. In particular, this book illustrates the challenges of resource management for IoV due to network dynamics, such as time-varying traffic intensity and vehicle mobility, and presents intelligent resource management solutions to adapt to the network dynamics. The Internet of Vehicles (IoV) enables vehicle-to-everything connectivity and supports a variety of applications for vehicles on the road

Intelligent resource management is critical for satisfying demanding communication and computing requirements on IoV, while the highly dynamic network environments pose challenges to the design of resource management schemes. This book provides insights into the significance of adaptive resource management in improving the performance of IoV. The customized communication protocol and computing scheduling scheme are designed accordingly by taking the network dynamics information as an integral design factor. Moreover, the decentralized designs of the proposed solutions guarantee low signaling overhead and high scalability.

A comprehensive literature review summarizing recent resource management schemes in IoV, followed by the customized design of communication and computing solutions for the two IoV use cases is included which can serve as a useful reference for professionals from both academia and industry in the area of IoV and resource management. Researchers working within this field and computer science and electrical engineering students will find this book useful as well.

Technische Details

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Autor

Mushu Li received the B.Eng. degree from the University of Ontario Institute of Technology (UOIT), Canada, in 2015, and the M.A.Sc. degree from Ryerson University, Canada, in 2017. She received the Ph.D. degree in Electrical and Computer Engineering from the University of Waterloo, Canada, in 2021. She is currently a Postdoctoral Fellow at Toronto Metropolitan University, ON, Canada. She was a Postdoctoral Fellow with the University of Waterloo, ON, Canada, from 2021 to 2022. Her research interests include mobile edge computing, the system optimization in wireless networks, and machine learning-assisted network management. She was the recipient of Natural Science and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship (2022), NSERC Canada Graduate Scholarship (2018), and Ontario Graduate Scholarship (2015 and 2016).

Jie Gao received the B.Eng. degree in electronics and information engineering from the Huazhong University ofScience and Technology, Wuhan, China, in 2007, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Alberta, Edmonton, AB, Canada, in 2009 and 2014, respectively. He was a postdoctoral fellow with Toronto Metropolitan University, Toronto, ON, Canada, from 2017 to 2019, a research associate with the University of Waterloo, Waterloo, ON, Canada, from 2019 to 2020, and an assistant professor with the Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, USA, from 2020 to 2022. Dr. Gao is currently an assistant professor in the School of Information Technology, Carleton University. His research interests include machine learning for communications and networking, network virtualization and digital twins, Internet of Things (IoT) and industrial IoT solutions, and next-generation wireless networks in general. He is a senior member of IEEE, the lead associate editor for the Vehicular Society Section within IEEE Access, and an editor for Peer-to-Peer Networking and Applications (Springer).

Xuemin (Sherman) Shen received the Ph.D. degree in electrical engineering from Rutgers University, New Brunswick, NJ, USA, in 1990. He is 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 networks. Dr. Shen 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 Member, and a Distinguished Lecturer of the IEEE Vehicular Technology Society and Communications Society.

Dr. Shen received the Canadian Award for Telecommunications Research from the Canadian Society of Information Theory (CSIT) in 2021, the R.A. Fessenden Award in 2019 from IEEE, Canada, Award of Merit from the Federation of Chinese Canadian Professionals (Ontario) in 2019, James Evans Avant Garde Award in 2018 from the IEEE Vehicular Technology Society, Joseph LoCicero Award in 2015 and Education Award in 2017 from the IEEE Communications Society, and Technical Recognition Award from Wireless Communications Technical Committee (2019) and AHSN Technical Committee (2013). He has also received the Excellent Graduate Supervision Award in 2006 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 IEEE Globecom' 16, IEEE Infocom'14, IEEE VTC'10 Fall, IEEE Globecom'07, and the Chair for the IEEE Communications Society Technical Committee on Wireless Communications. Dr. Shen is the President of the IEEE Communications Society. He was the Vice President for Technical & 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 of the ComSoc.

Lian Zhao received the Ph.D. degree from the Department of Electrical and Computer Engineering (ELCE), University of Waterloo, Canada, in 2002. She joined the Department of Electrical and Computer Engineering at Toronto Metropolitan University (formerly Ryerson University), Canada, in 2003. Her research interests are in the areas of wireless communications, resource management, mobile edge computing, caching and communications, and IoV networks.

She has been an IEEE Communication Society (ComSoc) and IEEE Vehicular Technology (VTS) Distinguished Lecturer (DL); received the Best Land Transportation Paper Award from IEEE Vehicular Technology Society in 2016, Top 15 Editor Award in 2016 for IEEE Transaction on Vehicular Technology, Best Paper Award from the 2013 International Conference on Wireless Communications and Signal Processing (WCSP), and the Canada Foundation for Innovation (CFI) New Opportunity Research Award in 2005. She has been serving as an Editor for IEEE Transactions on Wireless Communications, IEEE Internet of Things Journal, and IEEE Transactions on Vehicular Technology (2013-2021). She served as a co-Chair of Wireless Communication Symposium for IEEE Globecom 2020 and IEEE ICC 2018; Finance co-Chair for 2021 ICASSP; Local Arrangement co-Chair for IEEE VTC Fall 2017 and IEEE Infocom 2014; co-Chair of Communication Theory Symposium for IEEE Globecom 2013. She has severed as a panel expert in various federal, provincial, and international evaluation committees. She is a licensed Professional Engineer in the Province of Ontario and a senior member of the IEEE Communication Society and Vehicular Technology Society.

Produktbeschreibung

• Verlag: Springer International Publishing
• Seitenzahl: 82
• Erscheinungstermin: 4. Januar 2023
• Englisch
• ISBN-13: 9783031228605
• Artikelnr.: 67238342

• Verlag: Springer International Publishing
• Seitenzahl: 82
• Erscheinungstermin: 4. Januar 2023
• Englisch
• ISBN-13: 9783031228605
• Artikelnr.: 67238342