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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

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

Ata de Conferência Internacional

Título: IECON Proceedings (Industrial Electronics Conference)

Páginas: 4677-4682

42nd Conference of the Industrial Electronics Society, IECON 2016

24 October 2016 through 27 October 2016

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6