Optimal Flight Speed Scheduling and Battery Swapping in UAV-enabled Mobile Edge Computing

In long-distance and long-duration flight missions of unmanned aerial vehicles (UAVs), optimal scheduling of flight speed and energy replenishment is crucial to ensure flight efficiency and safety. This paper focuses on a UAV-based patrol inspection system, where a UAV is scheduled to visit multiple task nodes that are geographically distributed in the communication coverage of a base station (BS). The UAV hovers at each task node, performing data collection and data processing. The BS is equipped with a mobile edge computing (MEC) server and a battery swapping station, offering computation and energy support to the UAV. A decision-making model customized for the UAV is proposed, jointly optimizing flight speed selection, battery swapping, and task offloading to minimize the UAV’s total operational cost in its flight. By introducing virtual nodes in the flight network, we construct a unidirectional extended graph, based on which the original nonconvex cost minimization problem is reformulated to a tractable mixed-integer convex problem. Further, a fast heuristic based on analytical target cascading (ATC) is developed to obtain suboptimal solutions to large-scale problems. Results demonstrate that the proposed model can lower the UAV’s total operational cost by providing greater flexibility in terms of speed selection and battery swapping, and the proposed heuristic shows high computational efficiency for large-scale network scenarios.