Skip to main content
Log in

Online Distributed Fault Diagnosis in Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper presents an online fault diagnosis algorithm for wireless sensor networks. This work explicitly takes into account the possibility of faults in different sections of sensor networks and communication channel. The diagnostic local view is obtained by exploiting the spatially correlated sensor measurements. These local views are then disseminated using a spanning tree of cluster heads. Our algorithm is shown to be energy efficient as it works in conjunction with the normal network activities and requires minimum additional diagnostic messages to be exchanged. Simulation results show that the performance of our algorithm is less sensitive to the average node degree for a wide range of fault rates.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ghosh S. (2007) Distributed systems: An algorithmic approach. Computer and Information Science series. Chapman & Hall, London

    Google Scholar 

  2. Zorzi, M., Rao, R., & Milstein, L. (1995). On the accuracy of a first-order markov model for data transmission on fading channels (pp. 211–215).

  3. Chen G., Li C., Ye M., Wu J. (2009) An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks 15: 193–207

    Article  Google Scholar 

  4. Malek M. (1980) A comparison connection assignment for diagnosis of multiprocessor systems. ACM, New York, pp 31–36

    Google Scholar 

  5. Douglas M., Blough H. W. B. (1999) The broadcast comparison model for on-line fault diagnosis in multicomputer systems: Theory and implementation. IEEE Transactions on Computers 48(5): 470–493

    Article  Google Scholar 

  6. Xiaofan Yang G. M. M., Evans D. J. (2005) A comparison-based diagnosis algorithm tailored for crossed cube multiprocessor systems. Microprocessors and Microsystems 29(4): 169–175

    Article  Google Scholar 

  7. Xiaofan Yang Y. Y. T. (2007) Efficient fault identification of diagnosable systems under the comparison model. IEEE Transactions on computers 56(12): 1612–1618

    Article  Google Scholar 

  8. Sun-Yuan Hsieh Y. S. C. (2008) Strongly diagnosable product networks under the comparison diagnosis model. IEEE Transactions on Computers 57(6): 721–732

    Article  Google Scholar 

  9. Chang G. Y. (2010) (t, k)-diagnosability for regular networks. IEEE Transactions on Computers 59(9): 1153–1157

    Article  Google Scholar 

  10. Halunga S. V., Vizireanu N. (2010) Performance evaluation for conventional and mmse multiuser detection algorithms in imperfect reception conditions. Digital Signal Processing 20(1): 166–178

    Article  Google Scholar 

  11. Chessa S., Santi P. (2002) Crash faults identification in wireless sensor networks. Computer Communications 25(14): 1273–1282

    Article  Google Scholar 

  12. Chen, J., Kher, S., & Somani, A. (2006). Distributed fault detection of wireless sensor networks. In Proceedings of the workshop on dependability issues in wireless ad hoc networks and sensor networks (pp. 65–72). New York: ACM.

  13. Jiang P. (2009) A new method for node fault detection in wireless sensor networks. Sensors 9(2): 1282–1294

    Article  Google Scholar 

  14. Lee M. H., Choi Y. H. (2008) Fault detection of wireless sensor networks. Computer Communications 31(14): 3469–3475

    Article  Google Scholar 

  15. Choi J. Y., Yim S. J., Huh Y. J., Choi Y. H. (2009) A distributed adaptive scheme for detecting faults in wireless sensor networks. WSEAS Transactions On Communications 8: 269–278

    Google Scholar 

  16. Hsin C., Liu M. (2006) Self-monitoring of wireless sensor networks. Computer Communications 29(4): 462–476

    Article  Google Scholar 

  17. Miao, X., Liu, K., He, Y., Liu, Y., & Papadias, D. (2011). Agnostic diagnosis: Discovering silent failures in wireless sensor networks. In INFOCOM, 2011 proceedings IEEE (pp. 1548–1556). April.

  18. Xiao, X. Y., Peng, W. C., Hung, C. C., & Lee, W. C. (2007). Using sensorranks for in-network detection of faulty readings in wireless sensor networks. In Proceedings of the 6th ACM international workshop on Data engineering for wireless and mobile access (pp. 1–8). New York, NY, USA: ACM.

  19. Guo, S., Zhong, Z., & He, T. (2009). Find: Faulty node detection for wireless sensor networks. In: Proceedings of the 7th ACM conference on embedded networked sensor systems (pp. 253–266). New York, NY, USA: ACM.

  20. Gao J. L., Xu Y. J., Li X. W. (2007) Weighted-median based distributed fault detection for wireless sensor networks. Journal of Software 18(5): 1208–1217

    Article  MATH  Google Scholar 

  21. Krishnamachari B., Iyengar S. (2004) Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Transactions on Computers 53(3): 241–250

    Article  Google Scholar 

  22. Instruments, T. (2001). MSP430x13x, MSP430x14x mixed signal microcontroller, datasheet.

  23. AS, C. (2004). CC1000 Single chip very low power RF transceiver data sheet.

  24. Barborak M., Dahbura A., Malek M. (1993) The consensus problem in fault-tolerant computing. ACM Computing Survey 25: 171–220

    Article  Google Scholar 

  25. Gilbert E.N. (1960) Capacity of a burst-noise channel. Bell System Technical Journal 39: 1253–1265

    MathSciNet  Google Scholar 

  26. Elliott E. O. (1963) Estimates of error rates for codes on burst error channels. Bell System Technical Journal 42: 1977–1997

    Google Scholar 

  27. Heinzelman W., Chandrakasan A., Balakrishnan H. (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications 1(4): 660–670

    Article  Google Scholar 

  28. Ren S., Park J. (2009) Fault-tolerance data aggregation for clustering wireless sensor network. Wireless Personal Communications 51: 179–192

    Article  Google Scholar 

  29. Awwad S., Ng C., Noordin N., Rasid M. (2011) Cluster based routing protocol for mobile nodes in wireless sensor network. Wireless Personal Communications 61: 251–281

    Article  Google Scholar 

  30. Ferng H. W., Tendean R., Kurniawan A. (2012) Energy-efficient routing protocol for wireless sensor networks with static clustering and dynamic structure. Wireless Personal Communications 65: 347–367

    Article  Google Scholar 

  31. Bsoul, M., Al-Khasawneh, A., Abdallah, A., Abdallah, E., & Obeidat, I. (2012). An energy-efficient threshold-based clustering protocol for wireless sensor networks. Wireless Personal Communications 1–14. doi:10.1007/s11277-012-0681-8.

  32. Younis O., Fahmy S. (2004) Heed: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing 3(4): 366–379

    Article  Google Scholar 

  33. Hu L. (1993) Distributed code assignments for cdma packet radio networks. IEEE/ACM Transactions on Networking 1(6): 668–677

    Article  Google Scholar 

  34. Atwood, B., Warneke, B., & Pister, K. (2000). Preliminary circuits for smart dust. In Southwest symposium on mixed-signal design (pp. 87–92).

  35. Vieira, M. A. M., Coelho, C. N., Jr., da Silva, D. C., Jr., & da Mata, J. M. (2003). Survey on wireless sensor network devices. In IEEE conference proceedings on emerging technologies and factory automation (pp. 537–544).

  36. Elhadef M., Boukerche A., Elkadiki H. (2008) A distributed fault identification protocol for wireless and mobile ad hoc networks. Journal of Parallel and Distributed Computing 68(3): 321–335

    Article  MATH  Google Scholar 

  37. Chessa, S., & Santi, P. (2001). Comparison-based system-level fault diagnosis in ad hoc networks. In Proceedings of the 20th IEEE symposium on reliable distributed systems (pp. 257–266).

  38. Elhadef, M., Boukerche, A., & Elkadiki, H. (2006). Diagnosing mobile ad-hoc networks: two distributed comparison-based self-diagnosis protocols. In Proceedings of the 4th ACM international workshop on mobility management and wireless access (pp. 18–27). New York, NY, USA: ACM.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arunanshu Mahapatro.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mahapatro, A., Khilar, P.M. Online Distributed Fault Diagnosis in Wireless Sensor Networks. Wireless Pers Commun 71, 1931–1960 (2013). https://doi.org/10.1007/s11277-012-0916-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-012-0916-8

Keywords

Navigation