Multi-master and multi-slave oriented task offloading strategy for real time and low power Internet of Vehicles

With the rapid development of intelligent driving and on-board intelligent applications, the computing power of on-board units is gradually inadequate. Intelligent networked vehicles offloading tasks to cloud servers through the Internet of Vehicles is considered to be a promising method. However, long distance deployment of cloud servers and the instability of return links also bring high time delay. While Mobile Edge Computing (MEC) effectively solves this problem by deploying computing resources to the network edge. Therefore, based on the idea of mobile edge computing, this paper first constructs the local edge collaborative computing model. By comprehensively considering the factors such as user psychology, vehicle speed, acceleration, location, communication ability and computing ability, the utility function of task vehicle and service vehicle is established. Then, according to the Stackelberg game strategy, the interaction behavior between task vehicle and service vehicle is modeled, the Stackelberg cyclic iterative task offloading algorithm in the Internet of Vehicles environment is proposed. It is proved that there is a Nash equilibrium point between service vehicle and task vehicle. Finally, the simulation results show that the algorithm has achieved a balance between task delay and expense, task vehicle utility and service vehicle utility, and has higher performance than other algorithms.