Anomaly detection in wireless sensor networks using self-organizing map and wavelets
Supakit Siripanadorn, Wipawee Hattagam, Neung Teaumroong · 2010
Wireless Sensor Networks (WSNs) have been applied in agriculture monitoring to monitor and collect various physical attributes within a specific area. It is important to detect data anomalies to determine a suitable course of action. The underlying aim of this paper is therefore to propose an anomaly detection scheme which is able to detect anomalies accurately by means of exploiting both time and frequency characteristics of the data signals. 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. Experiment results from synthetic and real data 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.