Outlier Detection Using k-means Clustering and Lightweight Methods for Wireless Sensor Networks

被引:0
作者
Andrade, A. T. C. [1 ,2 ]
Montez, C. [1 ]
Moraes, R. [1 ]
Pinto, A. R. [1 ]
Vasques, Francisco [3 ]
da Silva, G. L. [1 ]
机构
[1] Univ Fed Santa Catarina, Florianopolis, SC, Brazil
[2] Fed Inst Catarinense, Camboriu, SC, Brazil
[3] Univ Porto, INEGI, Fac Engn, Oporto, Portugal
来源
PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2016年
关键词
event detection; outlier detection; wireless sensor network; information fusion; monitoring applications; FUSION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Wireless Sensor Networks (WSNs) are susceptible to faults both in sensors and in communication. Information fusion techniques allow to extract precise information from a large amount of data. Detection, identification and treatment of outlier, in these techniques, is a key point. Outlier detection in WSNs is a challenge due to the low capacity of the nodes and low bandwidth of the network. This paper proposes a methodology that applies the clustering and lightweight statistics techniques for detection of outliers in WSNs. The assessment of the methodology involves a case study with temperature sensors in WSN nodes. The results show that this methodology is able to provide precise information, even in the presence of outliers.
引用
收藏
页码:4677 / 4682
页数:6
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