Dynamic Offloading and Frequency Allocation for Internet of Vehicles with Energy Harvesting

The emerging vehicle services need more stable and efficient communication environments. Furthermore, fast-developing in-vehicle applications increase power consumption, and bring new challenges to the endurance of electric vehicles (EVs). In this paper, taking a vehicular network with energy harvesting as the background, we propose a joint online algorithm based on vehicle mobility to minimize the energy consumption of electric vehicles. Specifically, we determine the relationship between MEC computing power allocation and vehicle information (position and driving speed), and minimize vehicles' energy consumption while ensuring the completion rate of offloading task calculations. This problem is NP-hard, and we use the Lyapunov optimization to transform the original problem into a deterministic optimization problem, which has a coupling between the local calculation amount and the MEC calculation frequency allocation decision. Toward this end, we apply the Lagrangian duality method to decouple the problem, and propose a Joint Local Computing and CPU-cycle Frequency Allocation (JLCCFA) algorithm to obtain the approximate optimal solution of the original problem. The simulation experiment results show that JLCCFA can effectively reduce the energy consumption of vehicle users and maintain a small task queue backlog.