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The Failure Detection Method of WSN Based on PCA-BDA and Fuzzy Neural Network

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Abstract

The Failure Detection algorithm based on Fuzzy Neural Network (FDD-FNN) method is proposed for effective detection of Wireless Sensor Network (WSN) system failures. In this method, the failure detection method for WSN and the minimum deviation optimization model based on the principle component analysis-Bayes discriminant analysis (PCA-BDA) and feature information entropy are proposed, input layer, fuzzy layer, fuzzy rule layer and ambiguity layer are designed, and the algorithm processes is introduced. Finally, the effects of key factors of FDD-FNN were investigated using simulations and its performance was compared with those of conventional algorithms. The results indicated excellent adaptability of FDD-FNN.

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Acknowledgements

This work is supported by Special Funds of Applied Science and Technology Research and Development of Guangdong Province, China (Grant No. 2015B010128015).

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Authors and Affiliations

  1. School of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming, China

    Bing Xu

  2. School of Mathematics and Statistics, The University of Sheffield, Sheffield, S3 7RH, UK

    Xiaoping Zhang

  3. School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, China

    Liqun Liu

Authors
  1. Bing Xu
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  2. Xiaoping Zhang
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  3. Liqun Liu
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Correspondence to Liqun Liu.

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Xu, B., Zhang, X. & Liu, L. The Failure Detection Method of WSN Based on PCA-BDA and Fuzzy Neural Network. Wireless Pers Commun 102, 1657–1667 (2018). https://doi.org/10.1007/s11277-017-5225-9

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  • DOI: https://doi.org/10.1007/s11277-017-5225-9

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