The Development of a Wireless Sensor Network Sensing Node Utilising Adaptive Self-diagnostics

  • Hai Li
  • Mark C. Price
  • Jonathan Stott
  • Ian W. Marshall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4725)


In Wireless Sensor Network (WSN) applications, sensor nodes are often deployed in harsh environments. Routine maintenance, fault detection and correction is difficult, infrequent and expensive. Furthermore, for long-term deployments in excess of a year, a node’s limited power supply tightly constrains the amount of processing power and long-range communication available.

In order to support the long-term autonomous behaviour of a WSN system, a self-diagnostic algorithm implemented on the sensor nodes is needed for sensor fault detection. This algorithm has to be robust, so that sensors are not misdiagnosed as faulty to ensure that data loss is kept to a minimum, and it has to be light-weight, so that it can run continuously on a low power microprocessor for the full deployment period. Additionally, it has to be self-adapative so that any long-term degradation of sensors is monitored and the self-diagnostic algorithm can continuously revise its own rules to accomodate for this degradation. This paper describes the development, testing and implementation of a heuristically determined, robust, self-diagnostic algorithm that achieves these goals.


Wireless Sensor Network Fault Detection Solar Panel Sensor Fault Sensor Fault Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    PROSEN. Prosen project homepage (2007), [Online] available:
  2. 2.
    Caselitz, P., Giebhardt, J., Mevenkamp, M.: Application of condition monitoring systems in wind energy converters. In: European Wind Energy Conference (EWEC 1997), Dublin, October 1997 (1997)Google Scholar
  3. 3.
    Angeli, C., Chatzinikolaou, A.: On-line fault detection techniques for technical system: A survey. International Journal of Computer Science & Applications I(1), 12–30 (2004)Google Scholar
  4. 4.
    Roumeliotis, S.I., Sukhatme, G.S., Bekey, G.A.: Sensor fault detection and identification in a mobile robot. In: 1998 IEEE International Conference on Robotics and Automation, May 1998, pp. 2223–2228. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  5. 5.
    de Freitas, N., Dearden, R., Hutter, F., Morales-Menendez, R., Mutch, J., Poole, D.: Diagnosis by a waiter and a mars explorer. Proceedings of IEEE 92(3), 455–468 (2004)CrossRefGoogle Scholar
  6. 6.
    Goel, P., Dedeoglu, G., Roumeliotis, S.I., Sukhatme, G.S.: Fault detection and identification in a mobile robot using multiple model estimation and neural network. In: IEEE International Conference on Robotics and Automation, Leuven, Belgium, May 1998, IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  7. 7.
    Ramanathan, N., Balzano, L., Burt, M., Estrin, D., Harmon, T., Harvey, C., Jay, J., Kohler, E., Rothenberg, S., Srivastava, M.: Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks, Center for Embedded Networked Sensing, UCLA and Department of Civil and Environmental Engineering, MIT, Tech (April 2006)Google Scholar
  8. 8.
    Koushanfar, F., Potkonjak, M., Sangiovanni-Vincentelli, A.: On-line fault detection of sensor measurements. In: Sensors, 2003. Proceedings of IEEE, October 2003, pp. 974–979. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  9. 9.
    Bertrand-Krajewski, J., Bardin, J., Mourad, M., Beranger, Y.: Accounting for sensor calibration, data validation, measurement and sampling uncertainities in monitoring urban drainage systems. Water Science and Technology 47(2), 95–102 (2003)Google Scholar
  10. 10.
    Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: DIWANS 2006: Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks, Los Angeles, CA, USA, pp. 65–72 (2006)Google Scholar
  11. 11.
    Davis instruments, wireless vantage pro2 specifications (2006), [Online]. available:
  12. 12.
    CR200 Series Datalogger with Spread Spectrum Radio, Campbell Scientific, Inc. (2005)Google Scholar
  13. 13.
    RF401/RF411/RF416 Spread Spectrum Data Radio/Modem, Campbell Scientific, Inc. (2005)Google Scholar
  14. 14.
    Li, H., et al.: (In prep. 2007)Google Scholar
  15. 15.
    Stott, J.: Canterbury weather website (2006), [Online]. available:
  16. 16.
    Uk weather extremes (2007), [Online]. available:
  17. 17.
    Model 109 Temperature Probe User guide. Campbell Scientific Inc. (2002)Google Scholar
  18. 18.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical recipes in C. Cambridge university press, New York, USA (1992)zbMATHGoogle Scholar
  19. 19.
    Gumstix. Gumstix homepage (2007), [Online]. available:

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hai Li
    • 1
  • Mark C. Price
    • 1
    • 2
  • Jonathan Stott
    • 1
  • Ian W. Marshall
    • 1
  1. 1.Computing Laboratory, University of Kent, Canterbury, CT2 7NFUK
  2. 2.School of Physical Sciences, University of Kent, Canterbury, CT2 7NHUK

Personalised recommendations