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A lightweight anomaly detection framework for medical wireless sensor networks
| Venue: | In Proc. Wireless Comm. and Net. Conf |
| Citations: | 6 - 1 self |
BibTeX
@INPROCEEDINGS{Salem_alightweight,
author = {Osman Salem and Yaning Liu and Ahmed Mehaoua},
title = {A lightweight anomaly detection framework for medical wireless sensor networks},
booktitle = {In Proc. Wireless Comm. and Net. Conf},
year = {},
pages = {4358--4363}
}
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Abstract
Abstract—In this paper, we focus on online detection and isolation of erroneous values reported by medical wireless sensors. We propose a lightweight approach for online anomaly detection in collected data, able to raise alarms only when patients enter in emergency situation and to discard faulty measurements. The proposed approach is based on Haar wavelet decomposition and Hampel filter for spatial analysis, and on boxplot for temporal analysis. Our objective is to reduce false alarms resulted from unreliable measurements. We apply our proposed approach on real physiological data set. Our experimental results prove the effectiveness of our approach to achieve good detection accuracy with low false alarm rate.
