Minimizing the Number of Wireless Charging PAD for UAV-Based Wireless Rechargeable Sensor Networks

TL;DR

This paper introduces an adaptive scheme for deploying the minimal number of wireless charging pads in UAV-based sensor networks, improving efficiency and coverage in large or challenging environments.

Contribution

It proposes a novel CDC & DSC deployment scheme that optimizes PAD placement considering arbitrary network configurations and merges adjacent PADs for efficiency.

Findings

Fewer PADs are needed compared to existing algorithms.

The scheme adapts to various geographic distributions and network sizes.

Simulation results demonstrate improved deployment efficiency.

Abstract

In wireless rechargeable sensor networks (WRSNs), most of researches address energy scarcity by introducing one or multiple ground mobile vehicles to recharge energy-hungry sensor nodes. The charging efficiency is limited by the moving speed of ground chargers and rough environments, especially in large-scale scenarios or challenging scenarios such as separate islands. To address the limitations, some researchers consider replacing ground mobile chargers with lightweight unmanned aerial vehicles (UAVs) to support extremely large-scale scenarios, because of the UAV moving at higher speed without geographical limitation. Moreover, multiple automatic landing wireless charging PADs are deployed in the network to recharge UAVs automatically. In this work, we investigate the problem of introducing the minimal number of PADs in UAV-based WRSNs. We propose a novel and adaptive PAD deployment scheme named CDC & DSC that can adapt to arbitrary locations of the base station, arbitrary geographic distributions of sensor nodes, and arbitrary sizes of network areas. In the proposed scheme, we first obtain an initial PAD deployment solution by clustering nodes in geographic locations. Then, we propose a center shift combining algorithm to optimize this solution by shifting the location of PADs and attempting to merge the adjacent PADs. The simulation results show that compared to existing algorithms, our proposed scheme can use fewer PADs to charge the whole network.