{"title":"动态通信网络中的节点稳定性","authors":"Matthias R. Brust, S. Rothkugel, A. Andronache","doi":"10.1109/AMS.2007.73","DOIUrl":null,"url":null,"abstract":"Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. Neighboring devices elect one appropriate device as clusterhead. Due to the dynamic environment, clusterhead selection becomes an important issue. We consider the problem of appropriate clusterhead selection in wireless ad-hoc networks and sensor networks. This work analyzes synchronous as well as asynchronous heuristics for discovering nodes with prolonged topological stability. These nodes appear more appropriate to be elected as clusterheads, since the frequency of clusterhead re-election and re-clustering can be decreased. The heuristics described rely on 2-hop topological information and avoid any use of geographical data","PeriodicalId":198751,"journal":{"name":"First Asia International Conference on Modelling & Simulation (AMS'07)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Node Stability in Dynamic Communication Networks\",\"authors\":\"Matthias R. Brust, S. Rothkugel, A. Andronache\",\"doi\":\"10.1109/AMS.2007.73\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. Neighboring devices elect one appropriate device as clusterhead. Due to the dynamic environment, clusterhead selection becomes an important issue. We consider the problem of appropriate clusterhead selection in wireless ad-hoc networks and sensor networks. This work analyzes synchronous as well as asynchronous heuristics for discovering nodes with prolonged topological stability. These nodes appear more appropriate to be elected as clusterheads, since the frequency of clusterhead re-election and re-clustering can be decreased. The heuristics described rely on 2-hop topological information and avoid any use of geographical data\",\"PeriodicalId\":198751,\"journal\":{\"name\":\"First Asia International Conference on Modelling & Simulation (AMS'07)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First Asia International Conference on Modelling & Simulation (AMS'07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AMS.2007.73\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First Asia International Conference on Modelling & Simulation (AMS'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AMS.2007.73","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. Neighboring devices elect one appropriate device as clusterhead. Due to the dynamic environment, clusterhead selection becomes an important issue. We consider the problem of appropriate clusterhead selection in wireless ad-hoc networks and sensor networks. This work analyzes synchronous as well as asynchronous heuristics for discovering nodes with prolonged topological stability. These nodes appear more appropriate to be elected as clusterheads, since the frequency of clusterhead re-election and re-clustering can be decreased. The heuristics described rely on 2-hop topological information and avoid any use of geographical data