JVECORA: A Joint Game Theory-Based Algorithm for Efficient Offloading and Load Balancing in Vehicular Networks

This study focuses on a game theory-based resource allocation technique for the Internet of Vehicles (IoV) in sixth generation (6G) technology. We propose a Joint Vehicle-Edge-Cloud Offloading and Resource Allocation (JVECORA) algorithm based on game theory and queuing models, that involves multiple vehicles, two edge nodes, and one cloud server. This algorithm is deployed and designed to find the optimal offloading decisions for vehicles, minimizing the system’s total overhead by considering latency, energy consumption, and subscription fees. Based on a potential game theory, vehicles act as players competing and updating their offloading decisions among local, edge, and cloud computing under various traffic conditions and constraints. Through several iterations, we demonstrate that our proposed method outperforms the conventional techniques by 19.88%. The JVECORA algorithm significantly reduces the overhead and converges to a Nash Equilibrium (NE). Additionally, the M/M/1 queuing model is used to optimize resource allocation and achieve load balancing between edge nodes.