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

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Andrade, ATC (Author) Other; Montez, C (Author) Other; Moraes, R (Author) Other; Pinto, AR (Author) Other; Francisco Vasques (Author) FEUP; da Silva, GL (Author) Other

Conference proceedings International

Title: IECON Proceedings (Industrial Electronics Conference)

Pages: 4677-4682

42nd Conference of the Industrial Electronics Society, IECON 2016

24 October 2016 through 27 October 2016

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00M-AN0

DOI: 10.1109/iecon.2016.7794093

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6