Intelligent fault management system for wireless sensor networks with reduction of power consumption

In Wireless Sensor Networks (WSN), when a hardware error is not identified and promptly corrected, all monitoring of a sensor network is compromised. This work presents a new approach to centralized fault management system for 6LoWPAN WSN. The system is based on two fault detection levels. A first level is performed locally, by all sensors within the network, using statistical methods. The second level is performed by the base station, through an ensemble of Multilayer Perceptron type Artificial Neural Networks (ANN) classifiers. One of them is continuously trained with streaming data, while the other one is used to take actual decisions about fault detection. The inputs of these ANN are outputs from a Kalman Filter and from an accelerometer. Experimental results indicate that this approach is capable of reducing message traffic and power consumption within a WSN, keeping the detection accuracy rate higher than 97%.