Research on Distributed V2V Computation Offloading Method for Internet of Vehicles Blockchain

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 to share computation resources, making it difficult to adapt to dynamic vehicle environments. To address these problems, this paper investigates the distributed vehicle-to-vehicle (V2V) computation offloading problem, establishes a Markov decision process, and designs a deep reinforcement learning-based optimal task allocation strategy for computation offloading. This paper utilizes dynamic pricing to incentivize vehicle to share computation resources while considering task priorities and computation resource release mechanisms. Additionally, this paper embeds the offloading scheme into the blockchain, employs 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 method. 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%.

Keywords

• vehicular edge computing; computation offloading; deep reinforcement learning; blockchain