Intelligent Computing and Communication for the Internet of Vehicles

1 Introduction.- 1.1 Internet of Vehicles (IoV).- 1.2 IoV Use Cases.- 1.3 Network Characteristics in IoV.- 1.4 Outline of the Monograph.- References.- 2 Overview of Communication and Computing in IoV.- 2.1 Communication and Computing in IoV.- 2.2 V2X Communication Protocols.- 2.3 Vehicular Computing Resource Scheduling.- References.- 3 Protocol Design for Safety Message Broadcast.- 3.1 Communication in Safety Message Broadcast.- 3.2 Network Scenario.- 3.2.1 Basic Settings.- 3.2.2 Features of Safety Message Broadcast.- 3.2.3 Assumptions.- 3.3 CIDC: A Distributed and Adaptive MAC Design.- 3.3.1 Messaging Mechanism Design.- 3.3.2 Contention Intensity Estimation Method.- 3.3.3 Channel Access Mechanism Design.- 3.4 CIDC: Network-Perspective Modeling.- 3.5 CIDC: Performance Analysis.- 3.5.1 Basic Features.- 3.5.2 Packet-to-Slot Ratio.- 3.5.3 Contention Intensity and Contention Delay.- 3.5.4 Collision Conditions and Probability.- 3.6 Performance Evaluation.- vii.- viii Contents.- 3.6.1 Performance with Accurate Contention Intensity.- Estimation.- 3.6.2 A Comparison of Analytical and Numerical Results.- 3.6.3 Performance under Contention Intensity Estimation.- Errors.- 3.7 Summary.- References.- 4 Computing Scheduling for Autonomous Driving.- 4.1 Computing in Autonomous Driving.- 4.2 System Model.- 4.2.1 Network Model.- 4.2.2 Computing Scheduling Scenarios.- 4.2.3 Age of Computing Results.- 4.3 Adaptive Computing Resource Scheduling.- 4.3.1 Restless Multi-armed Bandit Formulation.- 4.3.2 Indexability and Index Policy.- 4.4 Machine Learning-based Scheduling Policy.- 4.4.1 DRL-assisted Scheduling.- 4.4.2 Scheduling Scheme for Asynchronous Offloading.- 4.5 Performance Evaluation.- 4.5.1 Numerical Results.- 4.5.2 Simulation in a Real Dataset.- 4.6 Summary.- References.- 5 Conclusions.- 5.1 Conclusions on Intelligent Computing and Communication.- in IoV.- 5.2 Open Research Problems.

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