{"title":"基于自组织路由和进化计算的无线传感器网络定位","authors":"J. Flathagen, R. Korsnes","doi":"10.1109/MILCOM.2010.5680202","DOIUrl":null,"url":null,"abstract":"We propose evolutionary computation to estimate positions of nodes within a sensor network. The approach uses signal strength measurements between nodes and given positions for a subset of these nodes (anchor nodes). The signal strength measurements and routing requests take place simultaneously. A data collecting unit (sink node) receives distance estimates which are input to the evolutionary algorithm projecting node positions. This evolutionary approach can sort out data outliers and hence produce robust estimates of node positions. The present work contributes to decrease the cost and complexity of applying sensor networks. The approach also provides redundancy for the node positioning where alternative methods fail. The present simulations show examples of network generation and routing combined with estimation of node positions.","PeriodicalId":330937,"journal":{"name":"2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Localization in wireless sensor networks based on Ad hoc routing and evolutionary computation\",\"authors\":\"J. Flathagen, R. Korsnes\",\"doi\":\"10.1109/MILCOM.2010.5680202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose evolutionary computation to estimate positions of nodes within a sensor network. The approach uses signal strength measurements between nodes and given positions for a subset of these nodes (anchor nodes). The signal strength measurements and routing requests take place simultaneously. A data collecting unit (sink node) receives distance estimates which are input to the evolutionary algorithm projecting node positions. This evolutionary approach can sort out data outliers and hence produce robust estimates of node positions. The present work contributes to decrease the cost and complexity of applying sensor networks. The approach also provides redundancy for the node positioning where alternative methods fail. The present simulations show examples of network generation and routing combined with estimation of node positions.\",\"PeriodicalId\":330937,\"journal\":{\"name\":\"2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MILCOM.2010.5680202\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MILCOM.2010.5680202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Localization in wireless sensor networks based on Ad hoc routing and evolutionary computation
We propose evolutionary computation to estimate positions of nodes within a sensor network. The approach uses signal strength measurements between nodes and given positions for a subset of these nodes (anchor nodes). The signal strength measurements and routing requests take place simultaneously. A data collecting unit (sink node) receives distance estimates which are input to the evolutionary algorithm projecting node positions. This evolutionary approach can sort out data outliers and hence produce robust estimates of node positions. The present work contributes to decrease the cost and complexity of applying sensor networks. The approach also provides redundancy for the node positioning where alternative methods fail. The present simulations show examples of network generation and routing combined with estimation of node positions.