Optimization of Models and Strategies for Computation Offloading in the Internet of Vehicles: Efficiency and Trust

Abstract

With the rapid development of the Internet of Vehicles (IoV), vehicles will generate massive data and computation demands, necessitating computation offloading at the edge. However, existing research faces challenges in efficiency and trust. In this paper, we explore the IoV computation offloading from both user and edge facility provider perspectives, working to optimize the quality of experience (QoE), load balancing, and success rate based on challenges to efficiency and trust. First, two vehicle interconnection models are constructed to extend the linkable range of intra-road and inter-road vehicles while considering the maximum link time constraint. Then, a dynamic planning method is proposed, combining the reputation and feedback mechanisms, which can schedule edge resources online based on the cumulative computation latency of each service side, reliability value, and historical behavior. These two phases further improve the efficiency of edge services. Subsequently, blockchain is combined to optimize the trust problem of edge collaboration, and an edge-limited Byzantine fault tolerance local consensus mechanism is proposed to optimize consensus efficiency and ensure the reliability of edge services. Finally, this paper conducts dynamic experiments on real-world datasets, verifying the effectiveness of the proposed algorithm and models in multiple vehicle density datasets and experimental scenarios.