车联网边缘计算中的多跳任务卸载决策OA

In Internet of Vehicle(IoV)edge computing offloading system,multi-hop offloading enables tasks to be off-loaded to vehicles outside the coverage area of road-side unit(RSU),effectively utilizing the computational re-sources of idle vehicles.However,the high-speed mobility of vehicles poses challenges to maintaining stability be-tween nodes.This paper proposes a centralized dynamic multi-hop offloading strategy based on the A*algorithm.The strategy aims to minimize task delay by modeling the problem as a Markov decision process(MDP).The A*algorithm is employed to determine the optimal task offloading queue for vehicles,and the proximal policy optimiza-tion(PPO)algorithm is utilized for problem-solving.Simulation results show that,compared with deep Q-learning and greedy strategies,the proposed method significantly improves task completion rates and reduces average delay by approximately 30％.