Applications of Game Theory in Vehicular Networks: A Survey

TL;DR

This survey reviews how game theory models are applied to address resource allocation, security, and cooperation challenges in vehicular networks within intelligent transportation systems, especially considering emerging 5G technologies.

Contribution

It provides a comprehensive overview of recent game theory applications in vehicular networks, highlighting models, challenges, and integration with 5G, which was lacking in prior literature.

Findings

Game theory effectively models strategic interactions in VNs.

GT applications improve resource management and security in VNs.

Integration with 5G enhances VNs performance and reliability.

Abstract

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 secure traffic and ubiquitous access to various applications. However, as the number of nodes in ITS increases, it is challenging to satisfy a varied and large number of service requests with different Quality of Service and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve either an individual or a group's objectives. 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 its 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 integrating with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions for applying GT in VNs are outlined.