关键词: 车辆边缘计算, 计算卸载, 深度强化学习, 区块链

Abstract: Vehicle edge computing enhances the computational capabilities of vehicles by offloading computations. The existing task offloading strategies do not simultaneously consider data security, priority of offloading tasks, computation resource release, and incentivizing vehicle-shared computation resources, making it difficult to adapt to dynamic vehicle environments. To address this, we investigate the distributed vehicle-to-vehicle (V2V) computation offloading problem, establish a Markov decision process, and design a deep reinforcement learning-based optimal task allocation strategy for computation offloading. We utilize dynamic pricing to incentivize vehicle-shared computation resources while considering task priorities and computation resource release mechanisms. Additionally, we embed the offloading scheme into the blockchain, employing the Vehicles Blockchain with identity authentication mechanisms to encrypt sensitive information such as vehicle information, task information, and transaction information, thus ensuring data security. Simulation experimental results validate the performance of the proposed approach. Compared with other algorithms, the average efficiency of the system is improved by at least 9.58% while the training time is saved by 10.28%.

Key words: Vehicular edge computing, Computing offloading, Deep Reinforcement Learning, Blockchain