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Reinforcement Learning-Enabled Resampling Particle Swarm Optimization for Sensor Relocation in Reconfigurable WSNs

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成果类型:
期刊论文
作者:
Wang, Minghua;Wang, Xingbin;Jiang, Kaiwu;Fan, Bo
作者机构:
[Wang, Minghua; Jiang, Kaiwu; Wang, Xingbin; Fan, Bo] Univ South China, Sch Elect Engn, Hunan Prov Key Lab Ultra Fast Micronano Technol &, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Sensors;Wireless sensor networks;Particle swarm optimization;Optimization;Sensor phenomena and characterization;Search problems;Learning automata;Reconfigurable wireless sensor networks;sensor relocation;resampling particle swarm optimization;learning automata
期刊:
IEEE Sensors Journal
ISSN:
1530-437X
年:
2022
卷:
22
期:
8
页码:
8257-8267
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61971215) 10.13039/100009377-Scientific Research Fund of Hunan Provincial Education Department (Grant Number: 21A0276) 10.13039/501100004735-Natural Science Foundation of Hunan Province (Grant Number: 2020JJ4526) Hunan Province Key Laboratory for Ultra-Fast Micro/Nano Technology and Advanced Laser Manufacture (Grant Number: 2018TP1041)
机构署名:
本校为第一机构
院系归属:
电气工程学院
摘要:
Aiming to maximize coverage performance and reduce the number of sensors deployed in the reconfigurable wireless sensor networks (RWSNs), in this paper, we first formulate a new cooperative sensing coverage control problem based on the confident information coverage model. Then, inspired by the reinforcement learning and resampling technology, a novel learning automata-based resampling particle swarm optimization (RPSOLA) algorithm is proposed to solve complex multi-peak optimization problem and optimize the cooperative sensing coverage control problem of RWSNs. Experimental results demonstrat...

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