Research Article
Wireless Charging Based Sensor Network Information Collection Through Unmanned Aerial Vehicles (UAVs)
@INPROCEEDINGS{10.1007/978-3-031-65126-7_36, author={Guoxin Xu and Jiawen Zhao and Xuehe Wang}, title={Wireless Charging Based Sensor Network Information Collection Through Unmanned Aerial Vehicles (UAVs)}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 19th EAI International Conference, QShine 2023, Shenzhen, China, October 8 -- 9, 2023, Proceedings, Part I}, proceedings_a={QSHINE}, year={2024}, month={8}, keywords={UAV Wireless Charging Clustering Data Collection}, doi={10.1007/978-3-031-65126-7_36} }- Guoxin Xu
Jiawen Zhao
Xuehe Wang
Year: 2024
Wireless Charging Based Sensor Network Information Collection Through Unmanned Aerial Vehicles (UAVs)
QSHINE
Springer
DOI: 10.1007/978-3-031-65126-7_36
Abstract
In recent years, the proliferation of commercial unmanned aerial vehicles (UAVs) has led to the widespread adoption of wireless charging technology, fostering their increasing application in various domains. This trend has made UAVs increasingly suitable for replacing conventional information collection vehicles in wireless sensor networks, particularly in scenarios where sensors possess both sensing and communication capabilities. In this paper, we discuss the minimum information collection time for a large-scale wireless sensor network consisting of multiple mission UAVs and one charging UAV. The mission UAV is responsible for collecting data from each sensor, and the wireless charging pile is used to replenish power to the mission UAVs, in order to minimize the completion time of the mission UAVs. First, a modifiedk-means++ clustering algorithm is utilized to assign sensor nodes with the number of clusters equal to the number of mission UAVs. The process of collecting sensor information within a certain range by the mission UAV is modeled as the traveling salesman problem (TSP). Then, we propose the concept of virtual center node which is found by using a gradient descent algorithm. We compare the performance of using a charging UAV to charge a mission UAV with three other methods, i.e., establishing a fixed charging pile to charge mission UAVs, and mission UAVs go back to original base station for charging, and combining both the charging UAV and a fixed charging pile. The experimental results show that the combination of a charging UAV and a charging pile outperforms the other three methods in reducing the completion time of mission UAV.
