The high mobility and flexible deployment capability of UAVs make them an impressive option for charging nodes in Wireless Rechargeable Sensor Networks (WRSNs) using Directional Wireless Power Transfer (WPT) technology. However, existing studies largely focus on 2D-WRSNs, lacking designs catering to real 3D-WRSNs. The spatial distribution characteristics of nodes in a 3D-WRSN further increase the complexity of the charging scheduling task, thus requiring a systematic framework to solve this problem. In this paper, we investigated the Directional UAV Charging Scheduling problem for 3D-WRSNs (DCS-3D) and established its NP-hard property, and then proposed a three-step framework named as directional charging scheduling algorithm using Functional Equivalent (FuncEqv) direction set and Lin-Kernighan heuristic (LKH) for 3D-WRSNs (FELKH-3D) to solve it. In FELKH-3D, the challenge of infinite charging direction space is solved by designing an algorithm generating a minimum-size direction set guaranteed to be FuncEqv to the infinite set of whole sphere surface, and the optimaility of the method was proved.To determine the optimal charging tour for the UAV, the LKH algorithm is employed.Simulation experiments demonstrated the superiority of FELKH-3D over other classical algorithms.
翻译:无人机的高机动性和灵活部署能力使其成为利用定向无线能量传输技术为无线可充电传感器网络节点充电的理想选择。然而,现有研究主要集中于二维无线可充电传感器网络,缺乏针对真实三维无线可充电传感器网络的设计。三维无线可充电传感器网络中节点的空间分布特性进一步增加了充电调度任务的复杂性,因此需要一个系统性的框架来解决此问题。本文研究了面向三维无线可充电传感器网络的定向无人机充电调度问题,并证明了其NP难特性,进而提出了一个名为FELKH-3D的三步框架来解决该问题。在FELKH-3D中,通过设计一种算法来生成一个最小尺寸的方向集,该方向集被保证与整个球面的无限方向集功能等效,从而解决了无限充电方向空间的挑战,并证明了该方法的最优性。为了确定无人机的最优充电路径,采用了Lin-Kernighan启发式算法。仿真实验证明了FELKH-3D相对于其他经典算法的优越性。