Optimizing 3D Charging Direction Set for Directional UAV Chargers with Dual-Conical Charging Beams in 3D-WRSNs

Document Type

Article

Publication Date

1-1-2026

Abstract

Wireless Rechargeable Sensor Networks (WRSNs) have become a key enabler of the Internet of Things. There are directional wireless chargers capable of emitting multiple charging beams to charge the nodes in WRSNs via Wireless Power Transfer technology (WPT); yet most existing research on antenna charging direction selection assumes a single-beam model, leading to the selection of a suboptimal antenna direction set. In addition, prior research mostly focuses on two-dimensional networks and lacks effective solutions for three-dimensional (3D) scenarios. In this paper, we investigate the Charging Scheduling problem using a Unmanned Aerial Vehicle (UAV) with Dual-Conical Charging Beams in 3D-WRSNs (CSUDB-3D) for charging the nodes in the network and prove it to be NP-hard. To address this challenge, we first tackle the problem of antenna direction set selection from the infinite-size entire 3D spherical direction set by elegantly designing an algorithm via exploiting the geometric properties among the nodes. We prove that this algorithm guarantees to return an antenna direction set with the minimum size that is functionally equivalent (FuncEqv)-meaning it ensures the same optimal scheduling performance-to the original infinite 3D spherical direction set, and hence name it the Minimum FuncEqv Direction Set Algorithm (MFEDS). Then, the Lin-Kernighan Heuristic (LKH) algorithm is adopted to determine a quasi-optimal charging tour for the UAV to charge the nodes. By integrating MFEDS and LKH, we build a three-step framework termed Scheduling of a UAV Charger with Dual-Conical Charging Beams in 3D-WRSNs (UAVDCB-3D) to effectively solve the CSUDB-3D problem. Simulation results demonstrate that UAVDCB-3D outperforms the best existing benchmark in terms of both total energy loss and time span. Specifically, it reduces total energy loss by up to 51.10% and the time span by up to 46.15%.

Publication Title

IEEE Transactions on Mobile Computing

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