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
Statistical analysis of network traffic for adaptive faults detection
IEEE Transactions on Neural Networks
Condition monitoring of 3G cellular networks through competitive neural models
IEEE Transactions on Neural Networks
A comparison between divergence measures for network anomaly detection
Proceedings of the 7th International Conference on Network and Services Management
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Wireless Sensor Networks (WSNs) have been developed and extensively applied in agriculture monitoring to monitor and collect various physical attributes within a specific area or environment of interest. Data readings from the sensors may be abnormal due to the sensors themselves such as limited battery power, onboard processing capability, 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. 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 are collected from WSNs.