Multi-AAV-Assisted On-Demand Charging in Dense Wireless Rechargeable Sensor Networks

Abstract

Wireless rechargeable sensor networks (WRSNs) have emerged as a promising solution to overcome the energy bottleneck in traditional battery-powered sensor networks. However, the uncertain energy demands and dense deployment of sensor nodes pose significant challenges to efficient charging scheduling in WRSNs. To address these challenges, this article proposes a novel multi-AAV assisted on-demand partial charging scheduling (MOPCS) algorithm. MOPCS integrates the advantages of one-to-many charging, partial charging, and dynamic multi-AAV coordination to maximize the network lifetime and energy utilization. The key contributions of this work include a real-time adaptive charging scheduling trigger mechanism, an energy-efficient charging cluster division method, a spatiotemporally balanced task allocation among multiple autonomous aerial vehicles (AAVs), and a hybrid priority-based charging path planning algorithm. Extensive simulations demonstrate that MOPCS significantly outperforms state-of-the-art algorithms in terms of charging request response timeliness, node survival rate, and AAV energy efficiency, especially in dense network deployments. This work provides valuable insights and practical solutions for the design and optimization of AAV-assisted charging scheduling in WRSNs, paving the way for more sustainable and scalable wireless sensor networks in various application scenarios.