{"title":"Co-SRL: A Convex Optimization Algorithm for Anchor Localization in Wireless Sensor Networks","authors":"Wu Liu , Donghong Sun , Ping Ren , Yihui Zhang","doi":"10.1016/j.aasri.2013.10.059","DOIUrl":null,"url":null,"abstract":"<div><p>This paper proposed a Convex Optimization method which is called Co-SRL and is used to localize sensor location in Wireless Sensor Networks.Co-SRL can be used to help the node to localize a friendnode or mobile node using anchors. In Co-SRL, convex optimization algorithm is used forthe estimationof malicious nodeposition.Simulation result shows that Co-SRL is both secure and robust, in an environment without colluding, Co-SRLcan identify more than half of the malicious nodes; and in an environment with colluding, no more than 15% of malicious nodescan escape from the identification of our methods.</p></div>","PeriodicalId":100008,"journal":{"name":"AASRI Procedia","volume":"5 ","pages":"Pages 62-66"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aasri.2013.10.059","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AASRI Procedia","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212671613000607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper proposed a Convex Optimization method which is called Co-SRL and is used to localize sensor location in Wireless Sensor Networks.Co-SRL can be used to help the node to localize a friendnode or mobile node using anchors. In Co-SRL, convex optimization algorithm is used forthe estimationof malicious nodeposition.Simulation result shows that Co-SRL is both secure and robust, in an environment without colluding, Co-SRLcan identify more than half of the malicious nodes; and in an environment with colluding, no more than 15% of malicious nodescan escape from the identification of our methods.