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Sensors 2015, 15(4), 8764-8786; doi:10.3390/s150408764

Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

1
School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
2
Department of Defence, Defence Science and Technology Organization, SA 5111, Australia
3
School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Nauman Aslam
Received: 29 August 2014 / Revised: 25 March 2015 / Accepted: 1 April 2015 / Published: 15 April 2015
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
View Full-Text   |   Download PDF [1289 KB, uploaded 15 April 2015]   |  

Abstract

Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). View Full-Text
Keywords: wireless sensor networks; healthcare; medical sensors; sensor fault; sensor anomaly detection; prediction wireless sensor networks; healthcare; medical sensors; sensor fault; sensor anomaly detection; prediction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Haque, S.A.; Rahman, M.; Aziz, S.M. Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare. Sensors 2015, 15, 8764-8786.

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