Hyunduk Kim, Chulki Kim, Jae-Hun Kim, M. Seo, Seok Lee, Taikjin Lee
{"title":"Distance Based Energy Efficient Clustering Method in Wireless Sensor Networks","authors":"Hyunduk Kim, Chulki Kim, Jae-Hun Kim, M. Seo, Seok Lee, Taikjin Lee","doi":"10.1109/CSE.2014.184","DOIUrl":null,"url":null,"abstract":"In the past decade, various hierarchical clustering methods have been proposed in order to efficiently minimize the energy consumption of sensor nodes. The energy consumption of such clustering methods heavily depends on the size of clusters and the number of member nodes. Existing methods form clusters by considering the distance between the sink node and each node. However, if clusters are formed based only on the distance, the numbers of nodes in clusters are not uniformly distributed, resulting in unbalanced cluster structures. To address the problem, we propose a method to build the optimal clusters by adjusting the size of clusters and number of nodes that are included, according to the distance between a sink node and normal nodes.","PeriodicalId":258990,"journal":{"name":"2014 IEEE 17th International Conference on Computational Science and Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 17th International Conference on Computational Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSE.2014.184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In the past decade, various hierarchical clustering methods have been proposed in order to efficiently minimize the energy consumption of sensor nodes. The energy consumption of such clustering methods heavily depends on the size of clusters and the number of member nodes. Existing methods form clusters by considering the distance between the sink node and each node. However, if clusters are formed based only on the distance, the numbers of nodes in clusters are not uniformly distributed, resulting in unbalanced cluster structures. To address the problem, we propose a method to build the optimal clusters by adjusting the size of clusters and number of nodes that are included, according to the distance between a sink node and normal nodes.