Joint Optimization of Energy and Delay in Task Offloading Process of Electric Connected Vehicles

The rapid development of 5G and battery has enabled the electricity-driven intelligent connected vehicles to become the focus of current automobile industry. With automobiles growing intelligent, convenient, and entertaining, the computation tasks generated by various vehicle-integrated applications significantly increase. Cloud servers far away from the vehicles cannot complete the users' tasks in time, while the energy and computing resources on current Electric Vehicles (EVs) are very limited. Multi-Access Edge Computing (MEC) has been proposed to process the tasks generated by vehicles, which can reduce the latency and save the battery energy of EVs. However, the computing resource of MEC servers is still limited and cannot meet the delay requirements if massive EVs all offload the tasks. In addition, due to the uneven spatial and temporal distribution of vehicle arrivals, some MEC servers are busy, while some others are idle, resulting in the low resource efficiency and task completion ratio. In this paper, we propose the mobility-aware task offloading strategy method to allocate the computation resource of roadside servers for multiple EVs. We formulate the mathematical model of task offloading and resource allocation to jointly optimize computation latency and EV energy. Finally, the discrete particle swarm optimization algorithm is used to solve the problem. Simulation results show that the proposed method significantly alleviates the energy consumption and reduce the latency compared with conventional methods.