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Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks

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Abstract

Wireless sensor networks (WSNs) are spatially distributed devices to support various applications. The undesirable behavior of the sensor node affects the computational efficiency and quality of service. Fault detection, identification, and isolation in WSNs will increase assurance of quality, reliability, and safety. In this paper, a novel neural network based fault diagnosis algorithm is proposed for WSNs to handle the composite fault environment. Composite fault includes hard, soft, intermittent, and transient faults. The proposed fault diagnosis protocol is based on gradient descent and evolutionary approach. It detects, diagnose, and isolate the faulty nodes in the network. The proposed protocol works in four phases such as clustering phase, communication phase, fault detection and classification phase, and isolation phase. Simulation results show that the proposed protocol performs better than the existing protocols in terms of detection accuracy, false alarm rate, false positive rate, and detection latency.

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Acknowledgements

All the simulations are carried out in Parallel Distributed Computing Laboratory at National Institute of Technology (NIT), Rourkela, India. The author is very much thankful to Tirtharaj Dash (BITS Goa, India), Sourav Kumar Bhoi, and Munesh Singh (NIT Rourkela, India) for their valuable suggestions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, 769008, India

    Rakesh Ranjan Swain & Pabitra Mohan Khilar

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  1. Rakesh Ranjan Swain
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  2. Pabitra Mohan Khilar
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Correspondence to Rakesh Ranjan Swain.

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Swain, R.R., Khilar, P.M. Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks. Wireless Pers Commun 95, 2507–2548 (2017). https://doi.org/10.1007/s11277-016-3931-3

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