Energy Efficient Resource Allocation and Trajectory Optimization in UAV-Assisted Mobile Edge Computing System

Mobile edge computing(MEC) has been considered as a promising technology with the ever-increasing computation demands, which offloads computation-intensive tasks to MEC servers to meet the low latency and high bandwidth requirements of the tasks. But considering the dynamic UEs, nonuniform distribution of task requests and the limitation of the dynamic of the fixed deployment of the edge severs, we investigate a Unmanned Aerial Vehicles (UAVs)-assisted edge computing system in this paper, where each UAV is equipped with server to assist local edge servers. Utilizing the mobility of UAVs provides flexible edge computing services. In this model, tasks are executed on the local edge server first. When the computing resources of the local edge server cannot meet the computational requirements of the task, the system will dispatch the UAVs. Task will be offloaded to UAV for execution. Considering that the endurance of UAVs is a tough problem under the current technical level. Our aim is to minimize the energy consumption of UAVs under the premise of satisfying the UEs demands as much as possible to achieve a higher resource utilization rate. We propose Tasks Offloading Policy Algorithm(TOPA) and Online UAVs Dispatching Base on the Shortest Distant Algorithm(ODSH). Simulation results show the effectiveness of the proposed of algorithms.