Fault Diagnosis of Node in WSN Based on Integration of Rough Sets and Neural Network

In the paper, a novel fault diagnosis method integrating rough sets and neural network in wireless sensor network (WSN) is presented. Firstly, the decision-making table of fault diagnosis can be gained by application environment and fault characteristic in WSN. Secondly, the attribute reduction of the table of decision-making is implemented by inductive attribute reduction algorithm in rough sets theory. Finally, a set of method for fault classification is founded by hamming network. The result of simulation shows that this method has the feathers such as high veracity of diagnosis, a little expenditure of communication, low energy consumption, and strong robustness.