Title :
Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection
Author :
Moustapha, Azzam I. ; Selmic, Rastko R.
Author_Institution :
Louisiana Tech Univ., Ruston
fDate :
5/1/2008 12:00:00 AM
Abstract :
This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node´s dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous 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 a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme.
Keywords :
Kalman filters; backpropagation; fault diagnosis; recurrent neural nets; telecommunication computing; telecommunication network topology; wireless sensor networks; Kalman filter method; backpropagation NN; recurrent neural networks; sensor node fault detection; wireless sensor networks; Fault detection; modeling; neural networks (NNs); wireless sensor networks (WSNs);
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
DOI :
10.1109/TIM.2007.913803