Energy-Efficient Flight Scheduling and Trajectory Optimization in UAV-Aided Edge Computing Networks

Energy saving is a critical issue in UAV-aided edge computing for their limited battery storage. Most recent studies in UAV-aided edge computing have focused on reducing the energy consumption of UAVs during a single flight, but this approach may not be appropriate for scenarios with a large number of ground clients (GCs) that need to be served. In this study, a UAV-mounted edge node is deployed as a mobile edge computing platform to collect and process tasks generated from GCs. The goal of this work is to minimize the energy consumption of the UAV by optimizing the number of flights and the flight trajectories, while adhering to the deadline constraints of the GCs. In this work, we first formulate the problem in a simple scenario where the UAV has sufficient energy for the entire flight. We then propose an offline optimal trajectory-design algorithm with theoretical analysis, while considering the deadline constraints. Next, we propose an online approximation algorithm that is suitable for a multi-flight scenario, where UAVs need to perform multiple flights to complete all tasks for the GCs. We prove that the approximation ratio of our algorithm is 2, and the simulation results demonstrate that our algorithms outperform the compared algorithms.