Yan Ge, Zhi Zheng, Bo Yan, Jiao Yang, Yuxuan Yang, Huipeng Meng
{"title":"An RSSI-based localization method with outlier suppress for wireless sensor networks","authors":"Yan Ge, Zhi Zheng, Bo Yan, Jiao Yang, Yuxuan Yang, Huipeng Meng","doi":"10.1109/COMPCOMM.2016.7925097","DOIUrl":null,"url":null,"abstract":"A novel least square (LS) localization method for wireless sensor networks (WSNs) using received signal strength indicator (RSSI) is proposed in this paper. Unlike previous LS methods, the proposed method performs location calculation with the aid of the condition number of coordinate matrix to avoid the appearance of outliers. The threshold of condition number is introduced in this paper to avoid outliers while enhance the localization accuracy. Simulation results demonstrate that our approach can suppress outliers more efficiently, and improve the accuracy and stability of localization.","PeriodicalId":210833,"journal":{"name":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd IEEE International Conference on Computer and Communications (ICCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPCOMM.2016.7925097","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
A novel least square (LS) localization method for wireless sensor networks (WSNs) using received signal strength indicator (RSSI) is proposed in this paper. Unlike previous LS methods, the proposed method performs location calculation with the aid of the condition number of coordinate matrix to avoid the appearance of outliers. The threshold of condition number is introduced in this paper to avoid outliers while enhance the localization accuracy. Simulation results demonstrate that our approach can suppress outliers more efficiently, and improve the accuracy and stability of localization.