{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T13:13:56Z","timestamp":1726146836207},"reference-count":22,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T00:00:00Z","timestamp":1560816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Special Fund Project of Jilin Province School Co-Construction Plan,The High-Level Technology Innovation Team Building Project of Jilin University,The National Natural Science Foundation of China","award":["No.SXGJQY2017-9,No.2017TD-19,61771219"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-L\u00e9vy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using L\u00e9vy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.<\/jats:p>","DOI":"10.3390\/s19122735","type":"journal-article","created":{"date-parts":[[2019,6,19]],"date-time":"2019-06-19T06:42:46Z","timestamp":1560926566000},"page":"2735","source":"Crossref","is-referenced-by-count":48,"title":["A Virtual Force Algorithm-L\u00e9vy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization"],"prefix":"10.3390","volume":"19","author":[{"given":"Shipeng","family":"Wang","sequence":"first","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun 130012, China"}]},{"given":"Xiaoping","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun 130012, China"}]},{"given":"Xingqiao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun 130012, China"}]},{"given":"Zhihong","family":"Qian","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11047-015-9519-0","article-title":"An improved dynamic deployment method for wireless sensor network based on multi-swarm particle swarm optimization","volume":"16","author":"Ni","year":"2017","journal-title":"Nat. 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