RFDCS: A reactive fault detection and classification scheme for clustered wsns

Wireless sensor networks (WSNs) are resource-constrained, self-organizing systems that can operate in favorable as well as hostile environments. The deployment capabilities of WSNs, especially in inaccessible and hostile environments, have increased the confidence in the use of WSNs manifold. However, data delivery in sensor networks remains intrinsically faulty and unpredictable. Transmission of erroneous data in the network wastes limited precious resources of the network (such as nodes energy, bandwidth, etc.) and reduces the fidelity of the data. Timely identification of faulty nodes is essential to minimize their consequences, enhance data quality, improve decision making, and extend the lifetime of the network. This work proposes a scheme for detecting and classifying permanent, transient, and intermittent faults in clustered WSNs. The scheme is conversation efficient and reactive. The cluster heads silently analyze information obtained from the nodes during the regular operation for identifying any suspicious behavior. This is done by utilizing moving average and correlation in the obtained data. Cluster heads initiate fault identification process only when they find any suspicious behavior. The presented model localizes certain events which attenuate over distance and neutralize the impact of distance on fault detection accuracy. Thus, our fault diagnosis and classification process utilize correlations that exist in data and handle the problem of measurement variations due to the differences in positions of sensor nodes from the event location when events attenuate over distance. NS-2 based simulation is carried out to evaluate and validate the performance of the proposed scheme. The results show a significant improvement in the performance in terms of fault detection accuracy, false alarm rate, false negative rate, fault classification accuracy and communication overhead.