{"title":"一种从密集传感器网络中识别冗余传感器节点的分析框架","authors":"K. Sakib, Z. Tari, P. Bertók","doi":"10.1109/ICCITECHN.2010.5723852","DOIUrl":null,"url":null,"abstract":"Redundant node deployment has an impact on network lifetime because redundant nodes consume excess energy by performing unnecessary repetitious tasks. A distributed node redundancy identification method, called Self-Calculated Redundancy Check (SCRC), is proposed to eliminate redundant tasks. A grid is assumed over the field to help each node to calculate its own redundancy by checking the coverage degree of its sensing region. This optimises the active node set while providing complete network coverage and connectivity. An analytical framework is presented for SCRC using the expected value optimisation technique. The framework is used to predict potentially redundant nodes under various node distributions.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"An analytical framework for identifying redundant sensor nodes from a dense sensor network\",\"authors\":\"K. Sakib, Z. Tari, P. Bertók\",\"doi\":\"10.1109/ICCITECHN.2010.5723852\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Redundant node deployment has an impact on network lifetime because redundant nodes consume excess energy by performing unnecessary repetitious tasks. A distributed node redundancy identification method, called Self-Calculated Redundancy Check (SCRC), is proposed to eliminate redundant tasks. A grid is assumed over the field to help each node to calculate its own redundancy by checking the coverage degree of its sensing region. This optimises the active node set while providing complete network coverage and connectivity. An analytical framework is presented for SCRC using the expected value optimisation technique. The framework is used to predict potentially redundant nodes under various node distributions.\",\"PeriodicalId\":149135,\"journal\":{\"name\":\"2010 13th International Conference on Computer and Information Technology (ICCIT)\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 13th International Conference on Computer and Information Technology (ICCIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCITECHN.2010.5723852\",\"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 13th International Conference on Computer and Information Technology (ICCIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCITECHN.2010.5723852","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An analytical framework for identifying redundant sensor nodes from a dense sensor network
Redundant node deployment has an impact on network lifetime because redundant nodes consume excess energy by performing unnecessary repetitious tasks. A distributed node redundancy identification method, called Self-Calculated Redundancy Check (SCRC), is proposed to eliminate redundant tasks. A grid is assumed over the field to help each node to calculate its own redundancy by checking the coverage degree of its sensing region. This optimises the active node set while providing complete network coverage and connectivity. An analytical framework is presented for SCRC using the expected value optimisation technique. The framework is used to predict potentially redundant nodes under various node distributions.