Adaptive Learning-Based Multi-Vehicle Task Offloading

In vehicular mobile edge computing, vehicles can provide computing services via V2V communication. Vehicles offload the task to the vehicles which own computing resources at each time period. Most of the available literature focus on the task offloading to a single vehicle. In this work, we propose a multi-vehicle task offloading based on the multi-armed bandit theory to meet the real-time requirements in the dynamic environment. Specifically, we propose a system model for multi-vehicle task offloading with the help of V2V communication. Then, we put forward a multi-vehicle task offloading algorithm based on the adaptive learning. Finally, we perform simulations and the results confirm that the proposed algorithm can effectively reduce the offloading transmission delay and improve the resource utilization than the method of offloading tasks to a single vehicle.