{"title":"An optimal strategy for the deployment of sensor nodes in green buildings","authors":"Liping Wang, Xuecheng Zou, Q. Meng, Xiaoli Song","doi":"10.1109/ICICIP.2015.7388170","DOIUrl":null,"url":null,"abstract":"Wireless sensor networks contribute to significantly reduce energy consumption in green buildings. The sensor-node deployment is a crucial problem in WSNs. This paper formulates the deployment model of sensor nodes for target detection and location in a horizontal plane of a building and introduces an objective function with two constraints for the optimization problem. The problem is transformed into an unconstraint function by using penalty functions. The function acts as the fitness function in the improved adaptive binary particle swarm optimization algorithm. The simulation results show that the algorithm achieves the required target detection and location accuracy under the full coverage and a limited budget. Furthermore, the convergence rate and solution are better than the standard binary particle swarm optimization algorithm.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2015.7388170","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Wireless sensor networks contribute to significantly reduce energy consumption in green buildings. The sensor-node deployment is a crucial problem in WSNs. This paper formulates the deployment model of sensor nodes for target detection and location in a horizontal plane of a building and introduces an objective function with two constraints for the optimization problem. The problem is transformed into an unconstraint function by using penalty functions. The function acts as the fitness function in the improved adaptive binary particle swarm optimization algorithm. The simulation results show that the algorithm achieves the required target detection and location accuracy under the full coverage and a limited budget. Furthermore, the convergence rate and solution are better than the standard binary particle swarm optimization algorithm.