Fault Tolerance in Wireless Sensor Networks: Finding Primary Path

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)


Wireless sensor networks (WSN) are prone to be affected by faults which may be caused due to a variety of reasons, namely hardware malfunction, software problems, inadequate energy resources, and range or environmental hazards. A WSN is required to be equipped to handle such situations else would suffer an overall curtailment of the lifetime and ultimately not meet the required goal. Tolerance to faults thus forms one of the guiding parameters in WSN design. In this paper, we have proposed a method to find a reliable routing protocol using fuzzy logic based on Link Quality Indicator (LQI), Received Signal Strength Indicator (RSSI), and number of hops to the base station. Additional to the primary reliable path, each node has a secondary path which will be alternate path for each sensor in case of failure of the primary path. Implementation of this approach has been done as TinyOS module and evaluated through TOSSIM simulations. The experimental results show promising results in terms of packet delivery and reliability of the network.


Fuzzy logic Received signal strength indicator Link quality indicator 


  1. 1.
    Akkaya, K., Younis, M.: A survey on routing protocols for wireless sensor networks. Ad hoc Networks 3(3), 325–349 (2005)CrossRefGoogle Scholar
  2. 2.
    Weipeng, J., Qu, W., Yaqiu, L., Qianlong, Z.: A reliable primary backup routing algorithm in wireless sensor network. Phys. Procedia 24, 1462–1468 (2012)CrossRefGoogle Scholar
  3. 3.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000, p. 10. IEEE (2000)Google Scholar
  4. 4.
    Al-Karaki, J.N., Kamal, A.E.: Routing techniques in wireless sensor networks: a survey. IEEE Wirel. Commun. 11(6), 6–28 (2004)CrossRefGoogle Scholar
  5. 5.
    Ganesan, D., Govindan, R., Shenker, S., Estrin, D.: Highly-resilient, energy-efficient multipath routing in wireless sensor networks. ACMSIGMOBILE Mobile Comput. Commun. Rev. 5(4), 11–25 (2001)CrossRefGoogle Scholar
  6. 6.
    Chanak, P., Banerjee, I, Rahaman, H.: Distributed multipath fault tolerance routing scheme for wireless sensor networks. In: 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT), pp. 241–247. IEEE (2013)Google Scholar
  7. 7.
    Hammoudeh, M., Newman, R.: Adaptive routing in wireless sensor networks: Qos optimisation for enhanced application performance. Information Fusion (2013)Google Scholar
  8. 8.
    Parwekar, P., Reddy, R.: An efficient fuzzy localization approach in wireless sensor networks. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–6. IEEE (2013)Google Scholar
  9. 9.
    Alwan, H., Agarwal, A.: A survey on fault tolerant routing techniques in wireless sensor networks. In: SENSORCOMM’09. Third International Conference on Sensor Technologies and Applications, 2009, pp. 366–371. IEEE (2009)Google Scholar
  10. 10.
    Kim, S., Fonseca, R., Culler, D.: Reliable transfer on wireless sensor networks. In: IEEE SECON 2004. 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004, pp. 449–459. IEEE (2004)Google Scholar
  11. 11.
    Raj, A.S.A., Ramalakshmi, M.K., Priyadharsini, M.C.: A survey on classification of fault tolerance techniques available in wireless sensor network. In: International Journal of Engineering Research and Technology, vol. 3, no. 1 (January-2014). ESRSA Publications (2014)Google Scholar
  12. 12.
    Haider, T., Yusuf, M.: A fuzzy approach to energy optimized routing for wireless sensor networks. Int. Arab J. Inform. Technol. (IAJIT) 6(2), 179–186 (2009)Google Scholar
  13. 13.
    Manjunatha, P., Verma, A., Srividya, A.: Fuzzy based optimized routing protocol for wireless sensor networks. In: Advances in Wireless Sensors and Sensor Networks, pp. 273–282. Springer (2010)Google Scholar
  14. 14.
  15. 15.
  16. 16.
    Levis, P., Lee, N., Welsh, M., Culler, D.: Tossim: Accurate and scalable simulation of entire tinyos applications. In: Proceedings of the 1st international conference on Embedded networked sensor systems, pp. 126–137. CM (2003)Google Scholar
  17. 17.
    Rappaport, T.S. et al.: Wireless Communications: Principles and Practice, vol. 2. Prentice Hall PTR, New Jersey (2002)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Anil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia
  2. 2.GITAM UniversityVisakhapatnamIndia

Personalised recommendations