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Path Coverage Scheme Based on Fuzzy Particle Swarm Optimization
Algorithm for Directional Sensor Networks |
| ZHANG Juwei1,2,3, WANG Yu 1,3, YANG Ting4 |
1.Electrical Engineering College, Henan University of Science and Technology, Luoyang 471023
2.System Engineering Institute, Xi′an Jiaotong University, Xi′an 710049
3.Power Electronics Device and System Engineering Laboratory of Henan,Henan University of Science and Technology, Luoyang 471023
4.School of Electrical Engineering and Automation, Tianjin University, Tanjin 300072 |
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Abstract By utilizing fuzzy data fusion rules, a fuzzy perception model for directional sensor nodes is built to reduce the network uncertain region. Aiming at path coverage problems of directional sensor networks, a path coverage enhancement algorithm of the directional sensor networks based on fuzzy particle swarm optimization is proposed. The formed n-dimension problem is transformed to one-dimension problem to improve the coverage area of single sensor node and thereby increase the network coverage. For the directional sensor network nodes of adjustable perception direction, the simulation experiment is carried out by comparing the proposed algorithm with the existing algorithms under random deployment. The results show that the proposed algorithm can effectively improve the path coverage of the directional sensor networks, have a faster convergence rate and prolong the network life time.
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Received: 13 May 2016
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| Fund:Supported by National Natural Science Foundation of China(No.61304144,61172014,61040010), Aeronautical Science Foundation of China(No.20115142005), National International Science and Technology Cooperation Foundation(No.2013DFA11040) |
| About author:: (ZHANG Juwei(Corresponding author), born in 1978, Ph.D., associate professor. His research interests include sensor networks.)(WANG Yu, born in 1991, master student. His research interests include directional sensor networks.)(YANG Ting, born in 1979, Ph.D., profe-ssor. His research interests include wireless sensor networks.) |
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2017, Vol. 30 
