Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to a sensor node fault detection. Recurrent neural networks (RNNs) are used to model a sensor node, its dynamics, and interconnections with other sensor network nodes. The modeling approach is used for sensor node identification and fault detection. The input to the neural network is chosen to include delayed output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of backpropagation-type neural network. The input to the neural network and topology of the network are based on a general nonlinear dynamic sensor model. A simulation example has demonstrated effectiveness of the proposed scheme.