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Fuzzy-Based Flat Anomaly Diagnosis and Relief Measures in Distributed Wireless Sensor Network

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Abstract

This paper bestows a distributed adaptive scheme for diagnosing inaccurate data (anomaly) in wireless sensor networks. Faults occurring in sensor nodes are routine owing to the sensor device itself and the harsh environment in which the sensor nodes are deployed. It is mandatory for the WSNs to discover the anomaly and take actions to avoid further seediness of the network lifetime for confirming data accuracy. In this standpoint, we propose two perspectives for diagnosing and alleviating anomalies. The first view depicts input space partitioning by subtractive clustering method with robust density measure. Later, Takagi–Sugeno fuzzy inference model is applied for selection of several parameters and its membership functions, and rule-based construction is practiced to spot anomalies in distributed clustering wireless sensor network. By exploring combined correlation analysis with second perspective, the eliminated anomalous data are replaced by imputed data. Experimental results infer accuracy and reliability with a reduced amount of energy consumption than the state-of-the-art techniques.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

    Usman Barakkath Nisha & Natarajan Uma Maheswari

  2. Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

    Ramalingam Venkatesh

  3. Department of Computer Science and Engineering, SSCET, Palani, Tamilnadu, India

    Rabi Yasir Abdullah

Authors
  1. Usman Barakkath Nisha
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  2. Natarajan Uma Maheswari
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  3. Ramalingam Venkatesh
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  4. Rabi Yasir Abdullah
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Correspondence to Natarajan Uma Maheswari.

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Barakkath Nisha, U., Uma Maheswari, N., Venkatesh, R. et al. Fuzzy-Based Flat Anomaly Diagnosis and Relief Measures in Distributed Wireless Sensor Network. Int. J. Fuzzy Syst. 19, 1528–1545 (2017). https://doi.org/10.1007/s40815-016-0253-2

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  • DOI: https://doi.org/10.1007/s40815-016-0253-2

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