Fault detection methods for wireless sensor networks using neural networks

This paper presents the implementation of a neural network-based fault detection for wireless sensor networks (WSNs). There are discussed two proposed approaches: both a centralized and respectively distributed fault detection. The methods are validated using a wireless sensor network that takes environmental measurements every 60 seconds. There were considered various fault scenarios that were detected and corrected by the implemented structure. Neural networks (NNs) are used to model the environmental parameter's dynamics. The physical measurement is compared against the predicted value and a given threshold of error to determine sensor fault.