Zhang Chunyu
1.Henan University of Technology Key Laboratory of Grain Information Processing and Control of Ministry of Education, Zhengzhou 450001, China；2.Henan University of Technology Key Laboratory of Machine Perception and Intelligent System, Zhengzhou 450001, China；3.Henan University of Technology School of Information Science and Engineering, Zhengzhou 450001, ChinaLiu Xiaodong
HIKVISION Digital Technology Company Limited by Shares, Hangzhou 310000, ChinaSun LiJun
1.Henan University of Technology Key Laboratory of Grain Information Processing and Control of Ministry of Education, Zhengzhou 450001, China；2.Henan University of Technology Key Laboratory of Machine Perception and Intelligent System, Zhengzhou 450001, China；3.Henan University of Technology School of Information Science and Engineering, Zhengzhou 450001, ChinaChen Tianfei
1.Henan University of Technology Key Laboratory of Grain Information Processing and Control of Ministry of Education, Zhengzhou 450001, China；2.Henan University of Technology Key Laboratory of Machine Perception and Intelligent System, Zhengzhou 450001, China；3.Henan University of Technology School of Information Science and Engineering, Zhengzhou 450001, China1.Henan University of Technology Key Laboratory of Grain Information Processing and Control of Ministry of Education, Zhengzhou 450001, China;2.Henan University of Technology Key Laboratory of Machine Perception and Intelligent System, Zhengzhou 450001, China;3.Henan University of Technology School of Information Science and Engineering, Zhengzhou 450001, China;4.HIKVISION Digital Technology Company Limited by Shares, Hangzhou 310000, China
TP393
Coverage control is a fundamental and critical problem in many applications of wireless sensor networks. Aiming at the high dimensional optimization of sensor node deployment and the complexity of coverage area, a coverage optimization method based on cuckoo search algorithm with principal component analysis is proposed for wireless sensor network. Based on the standard cuckoo search (CS) algorithm, this algorithm adds the principal component analysis method to reduce the correlation between cuckoo individual position information and improve the exploration ability of the algorithm. Simulation results show that when the contribution rate is greater than 0.5, the PCA cuckoo search algorithm not only outperforms the standard CS algorithm in six benchmark test functions, but also can effectively improve the coverage area of nodes in wireless sensor network.
