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A Fuzzy Knowledge Based Fault Tolerance Mechanism for Wireless Sensor Networks

A Fuzzy Knowledge Based Fault Tolerance Mechanism for Wireless Sensor Networks

Sasmita Acharya (VSS University of Technology, Department of Computer Application, Burla, India) and C. R. Tripathy (VSS University of Technology, Department of Computer Science and Engineering, Burla, India)
Copyright: © 2018 |Pages: 18
DOI: 10.4018/IJRSDA.2018010107
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Abstract

Wireless Sensor Networks (WSNs) are the focus of considerable research for different applications. This paper proposes a Fuzzy Knowledge based Artificial Neural Network Routing (ANNR) fault tolerance mechanism for WSNs. The proposed method uses an exponential Bi-directional Associative Memory (eBAM) for the encoding and decoding of data packets and application of Intelligent Sleeping Mechanism (ISM) to conserve energy. A combination of fuzzy rules is used to identify the faulty nodes in the network. The Cluster Head (CH) acts as the data aggregator in the network. It applies the fuzzy knowledge based Node Appraisal Technique (NAT) in order to identify the faulty nodes in the network. The performance of the proposed ANNR is compared with that of Low-Energy Adaptive Clustering Hierarchy (LEACH), Dual Homed Routing (DHR) and Informer Homed Routing (IHR) through simulation.
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In this section, the techniques, relative advantages and disadvantages of different existing WSN fault tolerance mechanisms are discussed.

Studies on fault tolerance of wireless sensor networks indicate that most of the recent fault tolerance mechanisms are based on the re-transmission of the data packets in case of failure of a Primary Cluster Head (PCH) node (Babaie et al., 2010; Bagci and Yazici, 2010; Godbole, 2012). But these re-transmissions increase the network traffic and at peak load times may lead to network congestion. A fault-tolerant clustering of wireless sensor networks is discussed in (Gupta & Younis, 2003). A Cluster Based Energy Efficient and Fault Tolerant n-Coverage in Wireless Sensor Network is discussed in (Kumar & Nagarajan, 2013). The paper (Ozdemir & Xiao, 2009) provides a detailed survey on different secure data aggregation techniques for WSNs.

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