Obstacle-Aware Charging Pad Deployment in Large-Scale WRSNs: An Outside-to-Inside Onion-Peeling-like Strategy

This paper addresses the critical challenge of deploying a minimum number of wireless charging pads (WCPs) in obstacle-rich, large-scale Wireless Rechargeable Sensor Networks (WRSNs) to sustain drone operations. We assume a single base station, stationary sensors, convex polygonal obstacles that drones must avoid, and that both the base station and WCPs provide unlimited energy. To solve this, we propose the Outside-to-Inside Onion-Peeling (OIOP) strategy, a novel two-stage algorithm that prioritizes the coverage of the most remote sensors first and then refines the deployment by removing redundant pads while strictly adhering to obstacle constraints. Simulation results demonstrate OIOP’s superior efficiency: it reduces the number of required pads by approximately 10.83% ± 1.30% and 12.16% ± 1.59% compared to state-of-the-art methods (SMC and MC) and achieves execution times that are 58.02% ± 2.44% and 72.09% ± 2.88% faster, respectively. The algorithm also exhibits remarkable robustness, showing the smallest performance degradation as obstacle density increases.