Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks
This paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequenc...
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MDPI AG
2022-11-01
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Online Access: | https://www.mdpi.com/1424-8220/22/23/9215 |
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author | Ning Liu Chuanwen Luo Jia Cao Yi Hong Zhibo Chen |
author_facet | Ning Liu Chuanwen Luo Jia Cao Yi Hong Zhibo Chen |
author_sort | Ning Liu |
collection | DOAJ |
description | This paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequency (RF) signals, and a mobile unmanned vehicle (MUV)-loaded laser transmitter travels on a fixed path to charge the on-board energy-limited UAV when it arrives just below the UAV. Based on the system, we investigate the trajectory optimization of laser-charged UAVs for charging WRSNs (TOLC problem), which aims to optimize the flight trajectories of a UAV and the travel plans of an MUV cooperatively to minimize the total working time of the UAV so that the energy of every sensor is greater than or equal to the threshold. Then, we prove that the problem is NP-hard. To solve the TOLC problem, we first propose the weighted centered minimum coverage (WCMC) algorithm to cluster the sensors and compute the weighted center of each cluster. Based on the WCMC algorithm, we propose the TOLC algorithm (TOLCA) to design the detailed flight trajectory of a UAV and the travel plans of an MUV, which consists of the flight trajectory of a UAV, the hovering points of a UAV with the corresponding hovering times used for the charging sensors, the hovering points of a UAV with the corresponding hovering times used for replenishing energy itself, and the hovering times of a UAV waiting for an MUV. Numerical results are provided to verify that the suggested strategy provides an effective method for supplying wireless rechargeable sensor networks with sustainable energy. |
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institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-03-09T17:33:01Z |
publishDate | 2022-11-01 |
publisher | MDPI AG |
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series | Sensors |
spelling | doaj.art-9194b5fe01db471eb7c210cea44ac1622023-11-24T12:10:27ZengMDPI AGSensors1424-82202022-11-012223921510.3390/s22239215Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor NetworksNing Liu0Chuanwen Luo1Jia Cao2Yi Hong3Zhibo Chen4School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaThis paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequency (RF) signals, and a mobile unmanned vehicle (MUV)-loaded laser transmitter travels on a fixed path to charge the on-board energy-limited UAV when it arrives just below the UAV. Based on the system, we investigate the trajectory optimization of laser-charged UAVs for charging WRSNs (TOLC problem), which aims to optimize the flight trajectories of a UAV and the travel plans of an MUV cooperatively to minimize the total working time of the UAV so that the energy of every sensor is greater than or equal to the threshold. Then, we prove that the problem is NP-hard. To solve the TOLC problem, we first propose the weighted centered minimum coverage (WCMC) algorithm to cluster the sensors and compute the weighted center of each cluster. Based on the WCMC algorithm, we propose the TOLC algorithm (TOLCA) to design the detailed flight trajectory of a UAV and the travel plans of an MUV, which consists of the flight trajectory of a UAV, the hovering points of a UAV with the corresponding hovering times used for the charging sensors, the hovering points of a UAV with the corresponding hovering times used for replenishing energy itself, and the hovering times of a UAV waiting for an MUV. Numerical results are provided to verify that the suggested strategy provides an effective method for supplying wireless rechargeable sensor networks with sustainable energy.https://www.mdpi.com/1424-8220/22/23/9215laser-charged UAVwireless power transfermobile unmanned vehiclewireless rechargeable sensor networktrajectory optimization |
spellingShingle | Ning Liu Chuanwen Luo Jia Cao Yi Hong Zhibo Chen Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks Sensors laser-charged UAV wireless power transfer mobile unmanned vehicle wireless rechargeable sensor network trajectory optimization |
title | Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks |
title_full | Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks |
title_fullStr | Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks |
title_full_unstemmed | Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks |
title_short | Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks |
title_sort | trajectory optimization of laser charged uavs for charging wireless rechargeable sensor networks |
topic | laser-charged UAV wireless power transfer mobile unmanned vehicle wireless rechargeable sensor network trajectory optimization |
url | https://www.mdpi.com/1424-8220/22/23/9215 |
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