Skip to main content

Fault Detection in WSNs - An Energy Efficiency Perspective Towards Human-Centric WSNs

  • Conference paper
  • First Online:
Agent and Multi-Agent Systems: Technologies and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 38))

  • 1265 Accesses

Abstract

Energy efficiency is a key factor to prolong the lifetime of wireless sensor networks (WSNs). This is particularly true in the design of human-centric wireless sensor networks (HCWSN) where sensors are more and more embedded and they have to work in resource-constraint settings. Resource limitation has a significant impact on the design of a WSN and the adopted fault detection method. This paper investigates a number of fault detection approaches and proposes a fault detection framework based on an energy efficiency perspective. The analysis and design guidelines given in this paper aims at representing a first step towards the design of energy-efficient detection approaches in resource-constraint WSN, like HCWSNs.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barborak, M., Dahbura, A., Malek, M.: The consensus problem in fault-tolerant computing. ACM Comput. Surv. 25(2), 171–220 (1993)

    Article  Google Scholar 

  2. Bhatti, S., Xu, J., Memon, M.: Energy-aware fault-tolerant clustering scheme for target tracking wireless sensor networks. In: ISWCS Conference (2010)

    Google Scholar 

  3. Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: DIWANS Workshop, p. 65 (2006)

    Google Scholar 

  4. De, D.: A distributed algorithm for localization error detection-correction, use in in-network faulty reading detection: applicability in long-thin wireless sensor networks. In: IEEE WCNC Conference, pp. 1–6 (2009)

    Google Scholar 

  5. Dereszynski, E.W., Dietterich, T.G.: Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns. ACM TOSN (2011)

    Google Scholar 

  6. Dobson, S., Hughes, D.: An Error-free Data Collection Method Exploiting Hierarchical Physical Models of Wireless Sensor Networks

    Google Scholar 

  7. Farruggia, A., Vitabile, S.: A novel approach for faulty sensor detection and data correction in wireless sensor network. In: Conference on BWCCA (2013)

    Google Scholar 

  8. Gao, J., Wang, J., Zhang, X.: Hmrf-based distributed fault detection for wireless sensor networks. In: IEEE GLOBECOM Conference, pp. 640–644 (2012)

    Google Scholar 

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

    Google Scholar 

  10. Jurdak, R., Wang, X.R., Obst, O., Valencia, P.: Chapter 12 Wireless Sensor Network Anomalies: Diagnosis and Detection Strategies, pp. 309–325 (2011)

    Google Scholar 

  11. Kamal, A.R.M., Bleakley, C., Dobson, S.: Packet-level attestation (pla): a framework for in-network sensor data reliability. ACM Trans. Sen. Netw. 9(2) (2013)

    Google Scholar 

  12. Karim, L., Nasser, N.: Energy efficient and fault tolerant routing protocol for mobile sensor network. In: IEEE ICC Conference, pp. 1–5 (June 2011)

    Google Scholar 

  13. Khazaei, E., Barati, A., Movaghar, A.: Improvement of fault detection in wireless sensor networks. In: CCCM 2009. ISECS Colloquium, vol. 4 (2009)

    Google Scholar 

  14. Kim, D.J., Prabhakaran, B.: Motion fault detection and isolation in body sensor networks. In: IEEE PerCom Conference, pp. 147–155 (2011)

    Google Scholar 

  15. Lau, B.C., Ma, E.W., Chow, T.W.: Probabilistic fault detector for wireless sensor network. Expert Syst. Appl. 8, 3703–3711 (2014). Jun

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Liu, K., Ma, Q., Zhao, X., Liu, Y.: Self-diagnosis for large scale wireless sensor networks. In: Proceedings of IEEE INFOCOM (2011)

    Google Scholar 

  18. Lo, C., Liu, M., Lynch, J.: Distributive model-based sensor fault diagnosis in wireless sensor networks. In: IEEE DCOSS Conference (2013)

    Google Scholar 

  19. Lo, C., Liu, M., Lynch, J., Gilbert, A.: Efficient sensor fault detection using combinatorial group testing. In: International Conference on IEEE DCOSS (2013)

    Google Scholar 

  20. Lo, C., Lynch, J.P., Liu, M.: Pair-wise reference-free fault detection in wireless sensor networks. In: IPSN Conference, pp. 117–118. ACM, NY (2012)

    Google Scholar 

  21. Ma, Q., Liu, K., Miao, X., Liu, Y.: Sherlock is around: detecting network failures with local evidence fusion. In: INFOCOM 2012 (Mar 2012)

    Google Scholar 

  22. Mahapatro, A., Khilar, P.M.: Fault diagnosis in wireless sensor networks: a survey. IEEE Commun. Surv. tutorials 15(4), 2000–2026 (2013)

    Article  Google Scholar 

  23. Miao, X., Liu, K., He, Y., Liu, Y., Papadias, D.: Agnostic diagnosis: discovering silent failures in wireless sensor networks. In: IEEE INFOCOM (2011)

    Google Scholar 

  24. Nguyen, T.A., Bucur, D., Aiello, M., Tei, K.: Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in WSNs. In: SoICT, pp. 234–241. ACM Press, New York, USA (2013)

    Google Scholar 

  25. Ni, K., Pottie, G.: Bayesian selection of non-faulty sensors. In: IEEE International Symposium on Information Theory (ISIT 2007), pp. 616–620 (June 2007)

    Google Scholar 

  26. Ni, K., Pottie, G.: Sensor network data fault detection with maximum a posteriori selection and bayesian modeling. ACM Trans. Sen. Netw. 8(3) (2012)

    Google Scholar 

  27. Ni, K., Ramanathan, N., Chehade, M.N.H., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., Srivastava, M.: Sensor network data fault types. ACM Trans. Sen. Netw. 25:1, 25:29 (2009)

    Google Scholar 

  28. Nie, J., Ma, H., Mo, L.: Passive diagnosis for wsns using data traces. In: IEEE 8th Conference on Distributed Computing in Sensor Systems (DCOSS) (2012)

    Google Scholar 

  29. Pai, H.T.: Reliability-based adaptive distributed classification in wireless sensor networks. IEEE Trans. Veh. Technol. 59(9) (2010)

    Google Scholar 

  30. Paradis, L., Han, Q.: A survey of fault management in wireless sensor networks. J. Netw. Syst. Manage. 15(2), 171–190 (2007). Jun

    Article  Google Scholar 

  31. Ramassamy, C., Fouchal, H., Hunel, P., Vidot, N.: A pragmatic testing approach for wireless sensor networks. In: ACM Q2SWinet Conference, pp. 55–61 (2010)

    Google Scholar 

  32. Rodrigues, A., Camilo, T., Silva, J.S., Boavida, F.: Diagnostic Tools for Wireless Sensor Networks: A Comparative Survey, vol. 21 (Jun 2012)

    Google Scholar 

  33. Salem, O., Guerassimov, A., Mehaoua, A., Marcus, A., Furht, B.: Sensor fault and patient anomaly detection and classification in medical wireless sensor networks. IEEE ICC Conference, pp. 4373–4378 (2013)

    Google Scholar 

  34. Shakshuki, E.M., Xing, X., Sheltami, T.R.: An intelligent agent for fault reconnaissance in sensor networks. In: iiWAS Conference, pp. 139–146, ACM (2009)

    Google Scholar 

  35. Snoussi, H., Richard, C.: Wsn06-5: Distributed bayesian fault diagnosis in collaborative wireless sensor networks. In: IEEE GLOBECOM, pp. 1–6 (2006)

    Google Scholar 

  36. Taleb, A., Mathew, J., Pradhan, D.: Fault diagnosis in multi layered de bruijn based architectures for sensor networks. In: IEEE PERCOM Conference (2010)

    Google Scholar 

  37. Venkataraman, G., Emmanuel, S., Thambipillai, S.: A cluster-based approach to fault detection and recovery in wireless sensor networks. In: ISWCS (2007)

    Google Scholar 

  38. Warriach, E., Nguyen, T.A., Aiello, M., Tei, K.: A hybrid fault detection approach for context-aware wireless sensor networks. In: IEEE Conference on MASS (2012)

    Google Scholar 

  39. Yu, M., Mokhtar, H., Merabti, M.: A Survey on Fault Management in Wireless Sensor Networks (2007)

    Google Scholar 

  40. Zhuang, P., Wang, D., Shang, Y.: Distributed faulty sensor detection. In: IEEE GLOBECOM Conference, pp. 1–6 (2009)

    Google Scholar 

Download references

Acknowledgments

This paper has been supported by the China Scholarship Council during Yue Zhang’s visit at DTU in the context of the IDEA4CPS project and National Natural Science Foundation of China (No. 61361136002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicola Dragoni .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Orfanidis, C., Zhang, Y., Dragoni, N. (2015). Fault Detection in WSNs - An Energy Efficiency Perspective Towards Human-Centric WSNs. In: Jezic, G., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technologies and Applications. Smart Innovation, Systems and Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-19728-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19728-9_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19727-2

  • Online ISBN: 978-3-319-19728-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics