Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Implementation of intelligent active fault tolerant control system
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Anomaly detection in IP networks
IEEE Transactions on Signal Processing
Anomaly detection in wireless sensor networks
IEEE Wireless Communications
Advanced analysis methods for 3G cellular networks
IEEE Transactions on Wireless Communications
Condition monitoring of 3G cellular networks through competitive neural models
IEEE Transactions on Neural Networks
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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.