{"title":"Impact of grey wolf optimization on WSN cluster formation and lifetime expansion","authors":"Marwa Sharawi, E. Emary","doi":"10.1109/ICACI.2017.7974501","DOIUrl":null,"url":null,"abstract":"This work introduces a cluster head selection optimization model in wireless sensor networks (WSN). It applies the grey wolf optimization. The optimization of WSN cluster heads greatly influences the network life time. Grey wolf optimization(GWO) is a recently proposed optimizer that has a variety of successful applications. Therefore, adapted and applied in here to solve the CH selection problem. Suitable fitness function were employed to ensure coverage of the WSN and is fed to the GWO to find its optimum. Results of the introduced model is compared with the LEACH routing protocol. Four different deployments of the WSN are examined. Lifetime, residual energy and network throughput performance indicators are examined in our experiments as assessment indicators. The introduced system outperforms the LEACH in almost all topologies using the different indicators.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACI.2017.7974501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 31
Abstract
This work introduces a cluster head selection optimization model in wireless sensor networks (WSN). It applies the grey wolf optimization. The optimization of WSN cluster heads greatly influences the network life time. Grey wolf optimization(GWO) is a recently proposed optimizer that has a variety of successful applications. Therefore, adapted and applied in here to solve the CH selection problem. Suitable fitness function were employed to ensure coverage of the WSN and is fed to the GWO to find its optimum. Results of the introduced model is compared with the LEACH routing protocol. Four different deployments of the WSN are examined. Lifetime, residual energy and network throughput performance indicators are examined in our experiments as assessment indicators. The introduced system outperforms the LEACH in almost all topologies using the different indicators.