Fault Diagnosis Algorithm for WSN Based on Clustering and Credibility

  • Lidan Wang
  • Xin Xu
  • Xiaofei Zhang
  • Cheng-Kuan LinEmail author
  • Yu-Chee Tseng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11335)


Fault diagnosis is one of the challenging problems in wireless sensor network (WSN). This paper proposes a fault diagnosis algorithm based on clustering and credibility (FDCC). Firstly, the network is divided into several clusters according to both geographic positions and measurements of sensor nodes for the purpose of improving the accuracy of network diagnostic result. The process of clustering can be divided into five phases: region division, head selection, coarse clustering, coarse cluster merge and cluster adjustment. Then, in order to further improve the accuracy of diagnostic result, a credibility model based on historical diagnostic result and remaining energy is established for each neighbor node. At last, nodes with higher credibility are selected to participate in diagnostic process. Simulation results show that the proposed algorithm can guarantee higher diagnostic accuracy.


Fault diagnosis Sensor network Clustering Credibility model 


  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: Proceedings of Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks, pp. 65–73 (2006)Google Scholar
  3. 3.
    Gupta, G., Younis, M.: Fault-tolerant clustering of wireless sensor networks. Wirel. Commun. Netw. 3, 1579–1584 (2003)Google Scholar
  4. 4.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference (2000)Google Scholar
  5. 5.
    Julie, E.G., Tamilselvi, S., Robinson, Y.H.: Performance analysis of energy efficient virtual back bone path based cluster routing protocol for WSN. Wireless Pers. Commun. 91(3), 1171–1189 (2016)CrossRefGoogle Scholar
  6. 6.
    Krishnamachari, B., Iyengar, S.: Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor network. IEEE Trans. Comput. 53(3), 241–250 (2004)CrossRefGoogle Scholar
  7. 7.
    Lee, M.-H., Choi, Y.-H.: Fault detection of wireless sensor networks. Comput. Commun. 31(14), 3469–3475 (2008)CrossRefGoogle Scholar
  8. 8.
    Lin, C.-R., Liu, K.-H., Chen, M.-S.: Dual clustering: integrating data clustering over optimization and constraint domains. IEEE Trans. Knowl. Data Eng. 17(5), 628–637 (2005)CrossRefGoogle Scholar
  9. 9.
    Liu, K., Ma, Q., Zhao, X., Liu, Y.: Self-diagnosis for large scale wireless sensor networks. In: Proceedings of IEEE International Conference on Computer Communications, pp. 1539–1547 (2011)Google Scholar
  10. 10.
    Mahapatro, A., Khilar, P.M.: Detection of node failure in wireless image sensor networks. ISRN Sens. Netw. 2012, 8 p. (2012)Google Scholar
  11. 11.
    Mahapatro, A., Khilar, P.M.: Fault diagnosis in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 15(4), 2000–2026 (2013)CrossRefGoogle Scholar
  12. 12.
    Mahapatro, A., Khilar, P.M.: Online distributed fault diagnosis in wireless sensor networks. Wireless Pers. Commun. 71(3), 1931–1960 (2013)CrossRefGoogle Scholar
  13. 13.
    Shao, S., Guo, S., Qiu, X.: Distributed fault detection based on credibility and cooperation for WSNs in smart grids. Sensors 17(5), 983 (2017)CrossRefGoogle Scholar
  14. 14.
    Teng, Y.-H., Lin, C.-K.: A test round controllable local diagnosis algorithm under the PMC diagnosis model. Appl. Math. Comput. 244(2), 613–623 (2014)Google Scholar
  15. 15.
    Venkataraman, G., Thambipillai, S.: Energy-efficient cluster-based scheme for failure management in sensor networks. IET Commun. 2(4), 528–537 (2008)CrossRefGoogle Scholar
  16. 16.
    Wang, L.D., Zhang, X.F., Teng, Y.-H., Lin, C.-K.: Parallel and local diagnostic algorithm for wireless sensor networks. In: Proceedings of Asia-Pacific Network Operations and Management Symposium, pp. 334–347 (2017)Google Scholar
  17. 17.
    Wang, W., Wang, B., Liu, Z.: A cluster-based real-time fault diagnosis aggregation algorithm for wireless sensor networks. Inf. Technol. J. 10(1), 80–88 (2011)CrossRefGoogle Scholar
  18. 18.
    Wang, A., Heinzelman, W.B., Sinha, A., Chandrakasan, A.P.: Energy-scalable protocols for battery-operated microSensor networks. J. VLSI Signal Process. Syst. Signal Image Video Technol. 29(3), 223–237 (2001)CrossRefGoogle Scholar
  19. 19.
    Wei, L.-Y., Peng, W.-C.: Clustering spatial data with a geographic constraint: exploring local search. Knowl. Inf. Syst. 31(1), 153–170 (2012)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Xiao, X.-Y., Peng, W.-C., Hung, C.-C., Lee, W.-C.: Using sensor ranks for in-network detection of faulty readings in wireless sensor networks. In: Proceedings of 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 1–8 (2007)Google Scholar
  21. 21.
    Yao, Y., Yu, Z., Wang, G.: Clustering routing algorithm of self-energized wireless sensor networks based on solar energy harvesting. J. China Univ. Posts Telecommun. 22(4), 66–73 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lidan Wang
    • 1
  • Xin Xu
    • 1
  • Xiaofei Zhang
    • 1
  • Cheng-Kuan Lin
    • 1
    Email author
  • Yu-Chee Tseng
    • 2
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Department of Computer ScienceNational Chiao-Tung UniversityHsinchuTaiwan

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