{"title":"Energy efficient fault-tolerant clustering algorithm for wireless sensor networks","authors":"K. Nitesh, M. Azharuddin, P. K. Jana","doi":"10.1109/ICGCIOT.2015.7380464","DOIUrl":null,"url":null,"abstract":"Energy utilization and fault tolerance are the two major issues in the development of a large scale wireless sensor networks (WSNs). In this paper, we propose a distributed algorithm to design an energy efficient cluster base WSN. We also propose a local recovery mechanism for the orphan sensor nodes, which are generated due to the failure of any cluster head. The proposed algorithm is based on cost function which is formulated with several parameters such as residual energy, distance and cluster cardinality. The algorithm is simulated rigorously over several performance metrics and the results acquired are compared with some existing algorithms to demonstrate its effectiveness.","PeriodicalId":400178,"journal":{"name":"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Green Computing and Internet of Things (ICGCIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICGCIOT.2015.7380464","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
Energy utilization and fault tolerance are the two major issues in the development of a large scale wireless sensor networks (WSNs). In this paper, we propose a distributed algorithm to design an energy efficient cluster base WSN. We also propose a local recovery mechanism for the orphan sensor nodes, which are generated due to the failure of any cluster head. The proposed algorithm is based on cost function which is formulated with several parameters such as residual energy, distance and cluster cardinality. The algorithm is simulated rigorously over several performance metrics and the results acquired are compared with some existing algorithms to demonstrate its effectiveness.