Passive Diagnosis for WSNs Using Time Domain Features of Sensing Data

Due to the dynamic network topology and limit of resources, fault diagnosis for wireless sensor networks is difficult. The existing diagnostic methods consume a lot of communication bandwidth and node resources, which lead to heavy burden of the resources limited network. This paper presents a passive diagnosis method used for fault detection and fault classification based on the time domain features of sensing data (TDSD). Firstly, the feature extraction and analysis of the sensing data are carried out using one-dimensional discrete Gabor transform, and then the data are diagnosed and classified with Self-Organizing Maps (SOM) neural network; finally the current network status and identifying the fault cause are determined. The results show that, comparing with other methods, this method has fewer burdens in network communication, better diagnostic accuracy rate and classification results, and so forth, and it has a high diagnostic accuracy especially for both node fault and network fault.