Applications of Game Theory in Vehicular Networks: A Survey

• Published in: IEEE Communications surveys and tutorials Vol. 23; no. 4; pp. 2660 - 2710
• Main Authors: Sun, Zemin, Liu, Yanheng, Wang, Jian, Li, Guofa, Anil, Carie, Li, Keqiang, Guo, Xinyu, Sun, Geng, Tian, Daxin, Cao, Dongpu
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2021
• Subjects: 5G mobile communication; game theory; Privacy; Quality of service; Resource management; Security; Sun; Vehicular networks; Wireless networks
• ISSN: 1553-877X
• DOI: 10.1109/COMST.2021.3108466

In the Internet of Things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined.