Telematics task offloading based on improved gray wolf algorithm

With the rapid development of Internet of Things (IoT) technology, the number of in-vehicle applications has significantly increased, and these applications have high requirements for real-time performance and computing power. However, resource-constrained on-board units (OBUs) cannot handle such a large number of tasks in a short time. To address this issue, this paper proposes a vehicular network task offloading strategy based on the Grey Wolf Optimization algorithm (SAGWO) to achieve the optimal offloading of invehicle tasks. This strategy first models the time generated by local computing/communication and offloading to the edge server for in-vehicle tasks, establishes a comprehensive optimization utility function, and converts the offloading problem into a constrained optimization utility problem. It also introduces a simulated annealing mechanism to enhance the global search ability of the Grey Wolf Optimization algorithm, escape from the local optimal solution dilemma, and obtain the global optimal solution. Simulation results show that compared with other classical offloading algorithms, the SAGWO algorithm has superior performance in reducing task delay.