NDLI: Discrete Wavelet Transform and One-Class Support Vector Machines for anomaly detection in wireless sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Takianngam, S. Usaha, W.
Copyright Year	2011
Description	Author affiliation: School of Telecommunication Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand (Takianngam, S.; Usaha, W.)
Abstract	Data readings from wireless sensor networks (WSNs) may be abnormal due to detection of unusual phenomena, limited battery power, sensor malfunction, or noise from the communication channel. It is thus, important to detect such data anomalies available in WSNs to determine a suitable course of action. This paper proposes an integrated data compression and anomaly detection algorithm in WSNs which can detect anomalies accurately by employing half of sensor data measurement, instead of using all the sensor data measurement. The contribution of this paper centers on data compression by using Discrete Wavelet Transform (DWT) then feeding to anomaly detection by using One-Class Support Vector Machine (OCSVM). We tested our algorithm with several synthetic and real world datasets. The results showed that the proposed algorithm outperformed previous techniques in terms of near 100% detection rate and with marginal increase in false positive rates in presence of short and noise faults.
Starting Page	1
Ending Page	6
File Size	1058330
Page Count	6
File Format	PDF
ISBN	9781457721656
e-ISBN	9781457721663
DOI	10.1109/ISPACS.2011.6146181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-07
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Discrete wavelet transforms Decision support systems Temperature measurement Wireless sensor networks wireless sensor networks discrete wavelet transform self-organizing map one-class support vector machine data compression anomaly detection
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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