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

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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.

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References

  1. Wang, Y. H, & Cao, K. N. (2014). A proactive complex event processing method for large-scale transportation internet of things. International Journal of Distributed Sensor Networks. doi:10.1155/2014/159052.

  2. Balasubramaniam, S., & Kangasharju, J. (2013). Realizing the internet of nano things: Challenges, solutions, and applications. Computer, 46(2), 62–68.

    Article  Google Scholar 

  3. Gong, P., Chen, T. M., & Xu, Q. (2015). ETARP: An energy efficient trust-aware routing protocol for wireless sensor networks. Journal of Sensors. doi:10.1155/2015/469793.

  4. Ebrahimi, N., McCullough, K., & Xiao, Z. L. (2013). Reliability of sensors based on nanowire networks with either an equilateral triangle lattice or a hexagonal lattice structure. IEEE Transactions on Nanotechnology, 12(1), 81–95.

    Article  Google Scholar 

  5. Campobello, G., Leonardi, A., & Palazzo, S. (2012). Improving energy saving and reliability in wireless sensor networks using a simple CRT-based packet-forwarding solution. IEEE/ACM Transactions on Networking, 20(1), 191–205.

    Article  Google Scholar 

  6. Gilesh, M. P., & Hansdah, R. C. (2011). An Adaptive reliable transport protocol based on automatic resend reQquest (ASQ) technique for wireless sensor networks. In Proceedings of the 10th international conference on advanced information networking and applications (WAINA) (pp. 409–416). IEEE.

  7. Park, J., Jeong, J., Jeong, H., et al. (2014). Improving the packet delivery performance for concurrent packet transmissions in WSNs. IEEE Transactions on Communications Letters, 18(1), 58–61.

    Article  Google Scholar 

  8. Ing-Ray, C., Speer, A. P., & Eltoweissy, M. (2011). Adaptive fault-tolerant QoS control algorithms for maximizing system lifetime of query-based wireless sensor networks. IEEE Transactions on Dependable and Secure Computing, 8(2), 161–176.

    Article  Google Scholar 

  9. Wakamiya, N., & Murata, M. (2011). Autonomous and adaptive wireless networking with bio-inspired algorithms. In Proceedings of the 10th international symposium on the autonomous decentralized systems (ISADS) (pp. 597–602). IEEE.

  10. Le, Z., Becker, E., Konstantinides, D. G., et al. (2010). Modeling reliability for wireless sensor node coverage in assistive testbeds. In Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments.

  11. Bouabdallah, F., Bouabdallah, N., & Boutaba, R. (2013). Reliable and energy efficient cooperative detection in wireless sensor networks. Computer Communications, 36(5), 520–532.

    Article  Google Scholar 

  12. Shrestha, A., Xing, L., & Liu, H. (2007). Modeling and evaluating the reliability of wireless sensor networks. In Proceedings of Reliability and Maintainability Symposium (RAMS’07) (pp. 186–191).

  13. Luo, H., Tao, H. X., Ma, H. D., & Das, S. K. (2011). Data fusion with desired reliability in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(3), 501–513.

    Article  Google Scholar 

  14. Gungor, V. C., & Hancke, G. P. (2009). Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Transactions on Industrial Electronics, 56(10), 4258–4265.

    Article  Google Scholar 

  15. Hou, L. Q., & Bergmann, N. W. (2012). Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 61(10), 2787–2798.

    Article  Google Scholar 

Download references

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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Authors and Affiliations

  1. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Xueyuan Street, No. 180, Huixing Road, Zigong, 643000, People’s Republic of China

    Yinggao Yue

  2. School of Instrument Science and Engineering, Southeast University, Sipailou 2#, Xuanwu District, Nanjing, 210096, People’s Republic of China

    Yinggao Yue, Jianqing Li, Hehong Fan, Qin Qin, Le Gu & Li Du

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  1. Yinggao Yue
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  2. Jianqing Li
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  3. Hehong Fan
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  5. Le Gu
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  6. Li Du
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Correspondence to Jianqing Li.

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Yue, Y., Li, J., Fan, H. et al. Fault Prediction Based on the Kernel Function for Ribbon Wireless Sensor Networks. Wireless Pers Commun 97, 3277–3292 (2017). https://doi.org/10.1007/s11277-017-4361-6

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