Skip to main content
SpringerLink
Log in

Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Wireless sensor network (WSN) has become one of the most important technologies because of its reliable remote monitoring ability. As sensors are often deployed at remote and/or hazardous environments, it is important to be able to perform faulty sensor nodes self-diagnosing. In this paper, we formulate WSN faulty nodes identification as a pattern classification problem. This paper uses semi-supervised method for faulty sensor nodes classification. To enhance the learning performance, we also introduce a label propagation mechanism which is based on local kernel density estimation. The basic concept of the method is to estimate the posterior probability of a scene that belongs to normal or different faulty modes. In this paper, we implemented a software platform to study WSN under different number of sensor nodes and faulty conditions. Our experimental results show the proposed semi-supervised method is highly effective. Thorough comparative analyses with other state-of-art semi-supervised learning methods were included. The obtained results confirmed that our proposed algorithm can deliver improved classification performance for WSN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710. https://doi.org/10.1016/j.comnet.2010.05.003

    Article  Google Scholar 

  2. Aslan YE, Korpeoglu I, Ulusoy Ö (2012) A framework for use of wireless sensor networks in forest fire detection and monitoring. Comput Environ Urban Syst 36(6):614–625. https://doi.org/10.1016/j.compenvurbsys.2012.03.002

    Article  Google Scholar 

  3. Buratti C, Conti A, Dardari D, Verdone R (2009) An overview on wireless sensor networks technology and evolution. Sensors 9(9):6869–6896. https://doi.org/10.3390/s90906869

    Article  Google Scholar 

  4. Daliri ZS, Shamshirb S, Besheli MA (2011) Railway security through the use of wireless sensor networks based on fuzzy logic. Int J Phys Sci 6(3):448–458

    Google Scholar 

  5. Delalleau O, Bengio Y, Roux NL (2005) Efficient non-parametric function induction in semi-supervised learning. In: Proceedings of the tenth international workshop on artificial intelligence and statistics (AISTAT), pp 96–103

  6. Erdelj M, Mitton N, Natalizio E (2013) Applications of industrial wireless sensor networks. In: Güngör VÇ, Hancke GP (eds) Industrial wireless sensor networks: applications, protocols, and standards. CRC Press, London

    Google Scholar 

  7. Fukunaga K (1990) Introduction to statistical pattern classification. Academic Press, Cambridge

    MATH  Google Scholar 

  8. Gao Y, Ma J, Yuille AL (2017) Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans Image Process 26(5):2545–2560

    Article  MathSciNet  MATH  Google Scholar 

  9. Geeta D, Nalini N, Biradar RC (2013) Fault tolerance in wireless sensor network using hand-off and dynamic power adjustment approach. J Netw Comput Appl 36(4):1174–1185. https://doi.org/10.1016/j.jnca.2013.02.005

    Article  Google Scholar 

  10. Ji P, Zhao N, Hao S, Jiang J (2014) Automatic image annotation by semi-supervised manifold kernel density estimation. Inf Sci 281(Supplement C):648–660. https://doi.org/10.1016/j.ins.2013.09.016

    Article  Google Scholar 

  11. Krishnamachari B, Iyengar S (2004) Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans Comput 53(3):241–250. https://doi.org/10.1109/TC.2004.1261832

    Article  Google Scholar 

  12. Lau BC, Ma EW, Chow TWS (2014) Probabilistic fault detector for wireless sensor network. Expert Syst Appl 41(8):3703–3711. https://doi.org/10.1016/j.eswa.2013.11.034

    Article  Google Scholar 

  13. Nie F, Xiang S, Liu Y, Zhang C (2010) A general graph-based semi-supervised learning with novel class discovery. Neural Comput Appl 19(4):549–555. https://doi.org/10.1007/s00521-009-0305-8

    Article  Google Scholar 

  14. Nie F, Xu D, Li X, Xiang S (2011) Semisupervised dimensionality reduction and classification through virtual label regression. IEEE Trans Syst Man Cybernet Part B (Cybernetics) 41(3):675–685. https://doi.org/10.1109/TSMCB.2010.2085433

    Article  Google Scholar 

  15. Othman MF, Shazali K (2012) Wireless sensor network applications: a study in environment monitoring system. Proc Eng 41(Supplement C):1204–1210. https://doi.org/10.1016/j.proeng.2012.07.302

    Article  Google Scholar 

  16. Pérez A, Larrañaga P, Inza I (2009) Bayesian classifiers based on kernel density estimation: flexible classifiers. Int J Approx Reason 50(2):341–362. https://doi.org/10.1016/j.ijar.2008.08.008

    Article  MATH  Google Scholar 

  17. Ray S, Goel A, Chandra N (2011) Accident detection by wireless sensor network and sending rescue message with GPS. J Comput 3(11):69–73

    Google Scholar 

  18. Taneja J, Krioukov A, Dawson-Haggerty S, Culler D (2013) Enabling advanced environmental conditioning with a building application stack. In: 2013 international green computing conference proceedings, pp 1–10. https://doi.org/10.1109/IGCC.2013.6604519

  19. Wang F, Zhang C (2008) Label propagation through linear neighborhoods. IEEE Trans Knowl Data Eng 20(1):55–67. https://doi.org/10.1109/TKDE.2007.190672

    Article  Google Scholar 

  20. Wang J, Wang F, Zhang C, Shen HC, Quan L (2009) Linear neighborhood propagation and its applications. IEEE Trans Pattern Anal Mach Intel 31(9):1600–1615. https://doi.org/10.1109/TPAMI.2008.216

    Article  Google Scholar 

  21. Wang M, Hua XS, Mei T, Hong R, Qi G, Song Y, Dai LR (2009) Semi-supervised kernel density estimation for video annotation. Comput Vis Image Underst 113(3):384–396. https://doi.org/10.1016/j.cviu.2008.08.003

    Article  Google Scholar 

  22. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002

    Article  Google Scholar 

  23. You Z, Zhao X, Wan H, Hung WN, Wang Y, Gu M (2011) A novel fault diagnosis mechanism for wireless sensor networks. Math Comput Model 54(1):330–343. https://doi.org/10.1016/j.mcm.2011.02.018

    Article  MATH  Google Scholar 

  24. Yu M, Mokhtar H, Merabti M (2007) Fault management in wireless sensor networks. IEEE Wirel Commun 14(6):13–19. https://doi.org/10.1109/MWC.2007.4407222

    Article  Google Scholar 

  25. Zhang H, Ho JK, Wu QJ, Ye Y (2013) Multidimensional latent semantic analysis using term spatial information. IEEE Trans Cybernet 43(6):1625–1640

    Article  Google Scholar 

  26. Zhang H, Ji Y, Li J, Ye Y (2016) A triple wing harmonium model for movie recommendation. IEEE Trans Ind Inf 12(1):231–239

    Article  Google Scholar 

  27. Zhang H, Li J, Ji Y, Yue H (2017) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inf 13(2):616–624

    Article  Google Scholar 

  28. Zhao M, Chow TWS, Tang P, Wang Z, Guo J, Zukerman M (2017) Route selection for cabling considering cost minimization and earthquake survivability via a semi-supervised probabilistic model. IEEE Trans Ind Inf 13(2):502–511

    Article  Google Scholar 

  29. Zhao M, Chow TWS, Wu Z, Zhang Z, Li B (2015) Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction. Inf Sci 324:286–309

    Article  MATH  Google Scholar 

  30. Zhao M, Chow TWS, Zhang Z, Li B (2015) Automatic image annotation via compact graph based semi-supervised learning. Knowl Based Syst 76:148–165

    Article  Google Scholar 

  31. Zhao M, Zhang Z, Chow TWS (2012) Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction. Pattern Recognit 45(4):1482–1499

    Article  MATH  Google Scholar 

  32. Zhao M, Zhang Z, Chow TWS, Li B (2014) A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction. Neural Netw 55:83–97

    Article  MATH  Google Scholar 

  33. Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. In: Thrun S, Saul LK, Schölkopf B (eds) Advances in neural information processing systems, vol 16. MIT Press, Cambridge, pp 321–328

    Google Scholar 

  34. Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the twentieth international conference on international conference on machine learning, ICML’03, AAAI Press, pp 912–919. URL http://dl.acm.org/citation.cfm?id=3041838.3041953

Download references

Acknowledgements

The work is supported by the Project No. 7004439 of City University of Hong Kong. It is also partially supported by the National Natural Science Foundation of China under Grant No. 61601112, the Fundamental Research Funds for the Central Universities and DHU Distinguished Young Professor Program.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Donghua University, Shanghai, People’s Republic of China

    Mingbo Zhao

  2. Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, SAR, People’s Republic of China

    Zhaoyang Tian & Tommy W. S. Chow

Authors
  1. Mingbo Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaoyang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tommy W. S. Chow
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tommy W. S. Chow.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, M., Tian, Z. & Chow, T.W.S. Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation. Neural Comput & Applic 31, 4019–4030 (2019). https://doi.org/10.1007/s00521-018-3342-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3342-3

Keywords

Search

Navigation