Parked electric vehicle‐assisted distributed edge intelligence in the vehicle‐to‐grid platform

Abstract

The Internet of Electric Vehicles (IoEV) has emerged as a key component of future networks. However, some computation‐intensive vehicular applications cannot be executed locally owing to IoEV infrastructure limitations and computing resource bottlenecks. In this study, we propose a task offloading and resource management scheme based on Parked Electric Vehicle (PEV)‐assisted distributed edge intelligence (DEI), making use of underutilized PEV resources to handle offloaded tasks. In the proposed scheme, distributional reinforcement learning, the normalized average bargaining solution (NABS), and V2G charging scheduling are jointly combined to dynamically control availability prediction, resource sharing, and scheduling. The proposed scheme maximizes hybrid optimization benefits through PEV and edge server cooperation. Simulation results confirm performance improvements of 10%, 10%, and 15% in normalized service payoff, system throughput, and task failure rate, respectively, compared with existing benchmark protocols. Open issues and research directions for PEV‐DEI systems are also discussed.