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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2015

01.12.2015

Outlier Detection Approach Using Bayes Classifiers in Wireless Sensor Networks

verfasst von: Chafiq Titouna, Makhlouf Aliouat, Mourad Gueroui

Erschienen in: Wireless Personal Communications | Ausgabe 3/2015

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Abstract

Wireless sensor networks (WSN) have become a new information collection and monitoring solution for a variety of applications. Sensor nodes may occasionally produce incorrect measurements due to battery depletion, damage of device and other causes. Those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. To address the problem of outlier detection in WSN, we propose in this paper a two-level sensor fusion-based outlier detection technique for WSN. The first level of outlier is conducted locally inside the sensor nodes, while the second level is carried out in a level higher (e.g., in a cluster head or gateway). The proposed approach functionality was tested by simulation using a real sensed data obtained from Intel Berkeley Research Lab. The experiment results show that the approach achieved a high-level of detection accuracy and a low percentage of false alarm rate.
Vorheriger Artikel Location Management in IPv4 Networks for Fixed Mobility Pattern Users
Nächster Artikel On the Analysis of MIMO-ZF Receiver Over Fully Correlated MIMO Rayleigh Fading with LMMSE Channel Estimation

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Metadaten
Titel
Outlier Detection Approach Using Bayes Classifiers in Wireless Sensor Networks
verfasst von
Chafiq Titouna
Makhlouf Aliouat
Mourad Gueroui
Publikationsdatum
01.12.2015
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2015
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-015-2822-3

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