The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of COMMUNICATIONS
Transactions ID Number: 19-656
Full Name: Supakit Siripanadorn
Position: Student
Age: ON
Sex: Male
Address: Suranaree University of Technology,111 University Avenue, Muang district, Nakhon Ratchasima 30000
Country: THAILAND
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E-mail address: architect_ton@hotmail.com
Other E-mails: architect_ton@hotmail.com
Title of the Paper: anomaly detection in wireless sensor networks using self-organizing map and wavelets
Authors as they appear in the Paper: Supakit Siripanadorn, Wipawee Hattagam, Neung Teaumroong
Email addresses of all the authors: architect_ton@hotmail.com,wusaha@ieee.org,neung@sut.ac.th
Number of paper pages: 10
Abstract: This paper proposes an anomaly detection scheme which is able to detect anomalies accurately by employing only important features of data signals, instead of using all the sensor data traces. The contribution of this paper centers on anomaly detection by using Discrete Wavelet Transform (DWT) combined with a competitive learning neural network called self-organizing map (SOM) in order to accurately detect abnormal data readings while using just half of the data size. Experiment results from synthetic and real data injected with synthetic faults collected from a WSN show that the proposed algorithm outperforms the SOM algorithm by up to 18% and DWT algorithm by up to 35% in presence of bursty faults with marginal increase of false alarm rate. Furthermore, in the real-world datasets experiments show that our proposed algorithm can maintain acceptable anomaly detection accuracy as well as the SOM algorithm while using just half of the input data.
Keywords: Anomaly Detection, Discrete Wavelet Transform, Self-Organizing Map, Wireless Sensor Networks, Agriculture Monitoring.
EXTENSION of the file: .pdf
Special (Invited) Session: Anomaly Detection in Wireless Sensor Networks using Self-Organizing Map and Wavelets
Organizer of the Session: 635-398
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