UAV Charging Path Planning Based on Adaptive Hover-Improved Particle Swarm Algorithm
Pages 288 - 297
Abstract
When the large number of sensors in wireless rechargeable sensor network (WRSNS), for the traditional one-to-one charging mode of low efficiency, put forward an adaptive hover charging algorithm AHPA, to minimize the charging time in the target area, using the improved particle swarm algorithm for path planning.First, the sensor network in the target area is fully covered based on the minimum circle coverage algorithm, and the initial charging and suspension position of UAV is determined by the minimum circle coverage algorithm.During the process of charging, UAV automatically adjusts the hover position in 3D space according to the sensor position and power state in the region.In the simulation experiments, this algorithm is compared with the one-to-one charging method, the cluster partition algorithm and the node hover genetic algorithm, the results show that this algorithm has obvious advantages in the charging completion time.
References
[1]
Wang Ruchuan, Sun Lijuan.The Technology Of Wireless Sensor Network and Its Application[M].Beijing :Posts & Telecom Press, 2010.
[2]
Tian M,Jiao W,Liu J.The charging strategy of mobile charging vehicles in wireless rechargeable sensor networks with heterogeneous sensors[J]. IEEE Access,2020,8:73096-73110.
[3]
J. Xu, Y. Zeng and R. Zhang."UAV-enabled multiuser wireless power transfer: Trajectory design and energy optimization," 2017 23rd Asia-Pacific Conference on Communications (APCC), 2017, pp. 1-6,
[4]
N. W. Najeeb and C. Detweiler. UAV Based Wireless Charging of Sensor Networks Without Prior Knowledge[C]// 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS). Madrid:IEEE, 2018: 3151-3158.
[5]
J. Johnson, E. Basha and C. Detweiler. Charge selection algorithms for maximizing sensor network life with UAV-based limited wireless recharging[C]//2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and InformationProcessing.Melbourne:IEEE,2013:159-164.
[6]
Li L,Xu Yixiang,Hu Xiaoguang, et al.A New Rechargeable WSNs Based Multi-UAV Network and Topology Control Algorithm[C]//2015 IEEE 10th Conference on Industrial Electronics and Applications(ICIEA),Auckland:IEEE,2015:507-512.
[7]
Wang Zhifang, Zheng Lin, Li Xiaoji. Deployment optimization of chargers in wireless rechargeable sensor networks [J]. Computer Engineering, 2018,44 (05): 83-87 + 93.
[8]
Wang Yang, Shan Tianle, Zhao Chuanxin, UAV-assisted wireless rechargeable sensor network charging path planning scheme [J]. Small Microcomputer system, 2022:1-10.
[9]
Yuan Heng. Algorithm study of wireless rechargeable network path planning based on UAV [D]. University of ESTC, 2020.
[10]
WELZL E. Smallest enclosing disks ( balls and ellipsoids) ༻M༽/ /MAURER H. New ༲esults and New Trends in Computer Science. Berlin, Germany: Springer, 1991: 359-370.
[11]
XIE L,SHI Y,HOU Y T,et al.Multi-Node Wireless Energy Charging in Sensor Networks[J] .IEEE/ACM Transactionson Networking,2015,23(2):437-450.
[12]
He S, Chen J, Jiang F, Energy Provisioning in Wireless Rechargeable Sensor Networks[J].IEEE Transactions on Mobile Computing, 2013,12(10):1931-1942,.
[13]
Lyu J, Zeng Y, Zhang R, Placement Optimization of UAV-Mounted Mobile Base Stations[J]. IEEE Wireless Communication Letters, 2017, 21(3):604-607.
[14]
Xu Guoxun, Liang Xiaolong, Zhang Huaqiang, Simulation study of aviation cluster multi-target group attack path planning [J]. Computer Simulation, 2017,34 (06): 66-70 + 324.
[15]
Zhu Mingwei, Fan Yuanxun. Optimization design of shovel device based on LDWPSO algorithm [J]. Coal mine Machinery, 2018,39 (07): 8
Recommendations
Multi-UAV WRSN charging path planning based on improved heed and IA-DRL
AbstractAiming at the problem of how to improve the energy utilization rate of UAV (Unmanned Aerial Vehicle) in the charging process of wireless rechargeable sensor network, a charging path planning scheme for multi-UAV wireless rechargeable sensor ...
A novel efficient on demand charging schedule for rechargeable wireless sensor networks
AbstractThe use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MCVs) are used for charging sensors using wireless energy charging (WEC) technology. In on demand charging, a ...
Optimal path planning for drones based on swarm intelligence algorithm
AbstractRecently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown ...
Comments
Information & Contributors
Information
Published In
August 2022
309 pages
ISBN:9781450396912
DOI:10.1145/3556384
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 October 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
SPML 2022
SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning
August 4 - 6, 2022
Dalian, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 42Total Downloads
- Downloads (Last 12 months)18
- Downloads (Last 6 weeks)1
Reflects downloads up to 02 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format