International Journal of Fuzzy Systems

, Volume 19, Issue 5, pp 1528–1545 | Cite as

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

  • Usman Barakkath Nisha
  • Natarajan Uma Maheswari
  • Ramalingam Venkatesh
  • Rabi Yasir Abdullah


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.


Accuracy Anomaly detection Subtractive clustering Fuzzy Sensor networks Takagi–Sugeno model 


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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Usman Barakkath Nisha
    • 1
  • Natarajan Uma Maheswari
    • 1
  • Ramalingam Venkatesh
    • 2
  • Rabi Yasir Abdullah
    • 3
  1. 1.Department of Computer Science and EngineeringPSNA College of Engineering and TechnologyDindigulIndia
  2. 2.Department of Information TechnologyPSNA College of Engineering and TechnologyDindigulIndia
  3. 3.Department of Computer Science and EngineeringSSCETPalaniIndia

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