UAV Joint Scheduling for Optimizing Charging and Data Collection in Wireless Rechargeable Sensor Networks

To achieve the objectives of efficient wireless charging and timely data collection in Wireless Rechargeable Sensor Networks (WRSNs), this paper proposes a joint scheduling scheme for UAV that optimizes both charging and data collection tasks. First, the network area is divided into sparse and non-sparse grids. A Sparse Grid Fusion Algorithm based on Minimum Enclosing Circle (SGFMC) is introduced to reduce the number of UAV hovering points in sparse grids, thereby improving energy replenishment efficiency. Then, for non-sparse grids, a clustering approach based on minimizing energy radiation area is employed to shrink the energy coverage of UAV, effectively shortening the distance between the UAV and sensor nodes and enhancing energy efficiency. Next, the charging priority of cluster head nodes is determined by jointly considering their residual energy and the UAV’s vertical displacement during flight. This not only reduces UAV’s energy consumption during vertical movement but also ensures fairness in charging. A binary search-based allocation strategy is then applied to optimally distribute UAV energy among cluster head nodes. Furthermore, to minimize the energy consumption of cluster heads when transmitting data to the UAV during data collection, an adaptive UAV hovering altitude adjustment strategy is introduced. Simulation results demonstrate that, compared with existing algorithms such as HEC, HSA, and SA, the proposed SGFMC strategy significantly improves UAV flight distance efficiency and overall energy utilization.