Fault Diagnosis of a Wireless Sensor Network Using a Hybrid Method

  • Farzin Piltan
  • Jong-Myon KimEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 931)


This paper proposes a reliable, intelligent, model-based (hybrid) fault detection and diagnosis (FDD) technique for wireless sensor networks (WSNs) in the presence of noise and uncertainties. A wireless sensor network is a network formed by a large number of sensor nodes in which each node is equipped with a sensor to detect physical phenomena such as light, heat, pressure, and temperature. Increasing the number of sensor nodes can cause an increase in the number of faulty nodes, which adversely affects the quality of service (QoS). Herein, the WSN modeling is based on an adaptive method that combines the fuzzy C-means clustering algorithm with the modified auto-regressive eXternal (ARX) model and is utilized for fault identification in WSNs. The proposed adaptive nonlinear ARX fuzzy C-means (NARXNF) clustering technique obtains both an improved convergence and error reduction relative to that of the traditional fuzzy C-means clustering algorithm. In addition, the proportional integral (PI) distributed observation is used for diagnosing multiple faults, where the convergence, robustness, and stability are validated by a fuzzy linear matrix inequality (FLMI). To test the proposed method, this technique was implemented through simulation using Omnet++ and MATLAB.


Wireless sensor network Fault diagnosis Fuzzy C-means clustering ARX model PI distributed observation technique Reliability 



This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20181510102160, 20162220100050, 20161120100350, and 20172510102130). It was also funded in part by the Leading Human Resource Training Program of Regional Neo Industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016H1D5A1910564), and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927).


  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Wu, Y., Stankovic, J., He, T., Lin, S.: Realistic and efficient multi-channel communications in wireless sensor networks. In: Proceedings INFOCOM, pp. 1867–1875 (2008)Google Scholar
  3. 3.
    Chowdhury, K.R., Nandiraju, N., Chanda, P., Agrawal, D.P., Zing, Q.A.: Channel allocation and medium access control for wireless sensor networks. Ad-Hoc Netw. 7, 307–321 (2009)CrossRefGoogle Scholar
  4. 4.
    Jadav, P., Babu, V.K.: Fuzzy logic based faulty node detection in wireless sensor network. In: 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE (2017)Google Scholar
  5. 5.
    Chouikhi, S., et al.: Recovery from simultaneous failures in a large scale wireless sensor network. Ad Hoc Netw. 67, 68–76 (2017)CrossRefGoogle Scholar
  6. 6.
    Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques—Part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 62(6), 3757–3767 (2015)CrossRefGoogle Scholar
  7. 7.
    Keshtgari, M., Deljoo, A.: A wireless sensor network solution for precision agriculture based on zigbee technology. Wirel. Sens. Netw. 4, 25–30 (2012)CrossRefGoogle Scholar
  8. 8.
    Konstantinos, K., et al.: Topology optimization in wireless sensor networks for precision agriculture applications. In: International Conference on IEEE Sensor Technologies and Applications, Sensor Comm 2007 (2007)Google Scholar
  9. 9.
    Najeh, T., et al.: Input fault detection and estimation using PI observer based on the ARX-Laguerre model. Int. J. Adv. Manuf. Technol. 90(5–8), 1317–1336 (2017)CrossRefGoogle Scholar
  10. 10.
    Agrawal, S., Mohanty, S.R., Agarwal, V.: Bearing fault detection using Hilbert and high frequency resolution techniques. IETE J. Res. 61(2), 99–108 (2015)CrossRefGoogle Scholar
  11. 11.
    Anh, H.P.H., Nam, N.T.: Novel adaptive forward neural MIMO NARX model for the identification of industrial 3-DOF robot arm kinematics. Int. J. Adv. Robot. Syst. 9(4), 104–112 (2012)CrossRefGoogle Scholar
  12. 12.
    Alavandar, S., Nigam, M.J.: Neuro-fuzzy based approach for inverse kinematics solution of industrial robot manipulators. Int. J. Comput. Commun. Control 3(3), 224–234 (2008)CrossRefGoogle Scholar
  13. 13.
    Wu, L., et al.: Fault detection for underactuated manipulators modeled by Markovian jump systems. IEEE Trans. Ind. Electron. 63(7), 4387–4399 (2016)CrossRefGoogle Scholar
  14. 14.
    Al-Dabbagh, R.D., Kinsheel, A., Mekhilef, S., Baba, M.S., Shamshirband, S.: System identification and control of robot manipulator based on fuzzy adaptive differential evolution algorithm. Adv. Eng. Softw. 78, 60–66 (2014)CrossRefGoogle Scholar
  15. 15.
    Aleksovski, D., et al.: A comparison of fuzzy identification methods on benchmark datasets. IFAC-Papers on Line 49(5), 31–36 (2016)CrossRefGoogle Scholar
  16. 16.
    Hartmann, A., et al.: Identification of switched ARX models via convex optimization and expectation maximization. J. Process Control 28, 9–16 (2015)CrossRefGoogle Scholar
  17. 17.
    Lo, C., Lynch, J.P., Liu, M.: Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks. Mech. Syst. Sig. Process. 66, 470–484 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electrical EngineeringUniversity of UlsanUlsanSouth Korea

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