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15391 Adaptive and Online One-Class Support Vector Machine-based Outlier Detection Techniques for Wireless Sensor Networks
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Zhang, Yang and Meratnia, N. and Havinga, P.J.M. (2009) Adaptive and Online One-Class Support Vector Machine-based Outlier Detection Techniques for Wireless Sensor Networks. In: Proceedings of the IEEE 23rd International Conference on Advanced Information Networking and Applications Workshops/Symposia, 26-29 May 2009, Bradford, United Kingdom. pp. 990-995. IEEE Computer Society. ISBN 978-0-7695-3639-2

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Official URL: http://dx.doi.org/10.1109/WAINA.2009.200

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Abstract

Outlier detection in wireless sensor networks is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. A key challenge for outlier detection in wireless sensor networks is to adaptively identify outliers in an online manner with a high accuracy while maintaining the resource consumption of the network to a minimum. In this paper, we propose one-class support vector machine-based outlier detection techniques that sequentially update the model representing normal behavior of the sensed data and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. Experiments with both synthetic and real data show that our online outlier detection techniques achieve high detection accuracy and low false alarm rate.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:SENSEI: Integrating the PhySical with the Digital World of the Network of the Future
ID Code:15391
Status:Published
Deposited On:05 June 2009
Refereed:Yes
International:Yes
More Information:statisticsmetis

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