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Wireless Personal Communications

, Volume 97, Issue 3, pp 3277–3292 | Cite as

Fault Prediction Based on the Kernel Function for Ribbon Wireless Sensor Networks

  • Yinggao Yue
  • Jianqing LiEmail author
  • Hehong Fan
  • Qin Qin
  • Le Gu
  • Li Du
Article

Abstract

There exist several applications of wireless sensor networks in which the reliable operation can be crucial. Fault prediction is a critical problem in reliability theory for ribbon wireless sensor networks (RWSNs). Accurate fault prediction can effectively improve the availability of the WSNs system. In this paper, we evaluated the network performance for RWSNs, studied the basic theory of kernel functions, proposed a new failure prediction method based on kernel function, and selected the radial basis function as kernel function failure prediction models from two aspects of node hardware failures and network failures for fault prediction. Theoretical evidence and experimental results have shown that the proposed algorithmic prediction method has higher accuracy of 12 and 15% than that of GRNN and PNN respectively. Finally, we provided extensive numerical results to demonstrate the usage and efficiency of the proposed algorithms and complement our theoretical analysis.

Keywords

Ribbon wireless sensor networks Fault prediction Kernel function Reliability 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for helpful comments which helped them improve the technical quality of the paper. This study was supported by International S&T Cooperation Program of China (2015DFA10490), the Natural Science Foundation of China (61571113), Sichuan University of Science and Engineering talent introduction project (2017RCL10 and 2017RCL11), the Opening Project of Material Corrosion and Protection Key Laboratory of Sichuan province (2017CL09), the Opening Project of Artificial Intelligence Key Laboratory of Sichuan Province (2016RYJ01).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Yinggao Yue
    • 1
    • 2
  • Jianqing Li
    • 2
    Email author
  • Hehong Fan
    • 2
  • Qin Qin
    • 2
  • Le Gu
    • 2
  • Li Du
    • 2
  1. 1.Artificial Intelligence Key Laboratory of Sichuan ProvinceSichuan University of Science and EngineeringZigongPeople’s Republic of China
  2. 2.School of Instrument Science and EngineeringSoutheast UniversityNanjingPeople’s Republic of China

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