Consortium Blockchain-Based Computation Offloading Using Mobile Edge Platoon Cloud in Internet of Vehicles

The rapid advancement of intelligent vehicles is deemed crucial to the emergence of diverse compute-intensive applications of assisted driving, which consist of automatic driving, speed recognition, hybrid sensing data fusion, etc. Nevertheless, resources-constraint vehicles with high mobility cannot always meet the computing and communication demands when the above applications occur. Additionally, considering the expensive and inflexible deployment of edge servers, offloading application tasks to ``Edge'' in the vehicular networks is not always working well. To effectively mitigate the above issues, the complicated application tasks are motivated to offload to the vehicle platoon, where the vehicles travel synchronously in a string with small headway. Benefiting from the stable connectivity, adjustable mobility, and reasonable charge, the task vehicle would like to process the task by leveraging the idle resources of each platoon member (PM). To make more effective use of the resources on the mobile edge platoon cloud (MEPC), we investigate the resource allocation strategy based on the task vehicle's service pricing strategy in this work. We first formulate the interactions between MEPC and task vehicle as a Stackelberg game to study the joint utility maximization of the MEPC and task vehicle. Then the Stackelberg Equilibrium (SE) for the proposed game is characterized and proved. The proposed algorithm Hook-Jeeves-based Stackelberg game (HJSG) can reach the SE. Finally, we introduce the consortium blockchain to ensure the security and privacy of service transactions. The entire system helps enhance task processing efficiency, protect transaction data, and improve service experience. Experimental results over numerical simulation based on practical scenarios demonstrate that compared with Multi-round Stackelberg Game (MRSG), uniform pricing, and the local computation strategy, the proposed HJSG algorithm can attain less execution time and faster convergence performance.