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Distributed Energy Efficiency for Fault Tolerance Using Cuckoo Search in Wireless Sensor Networks

  • N. PriyaEmail author
  • P. B. Pankajavalli
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)

Abstract

A Wireless Sensor Network (WSN) is a self-organized network, which consists of thousands of in-expensive and low powered devices and these devices are highly energy constrained. Therefore, energy plays a vital role in communication between the sensors nodes as the livelihood of the nodes may get affected with lack of energy. Hence energy efficiency is mandatory for maintaining the network longevity. The proposed Distributed Energy Efficient (DEE) fault tolerance clustering using cuckoo search (CS) is having the ability of tolerating the failure of sensor nodes that will ensure the uninterrupted communication. The performance evaluation of the DEE with the existing algorithms such as Particle swarm optimization (PSO), Bee colony optimization (BCO), and Fire fly optimization (FFO) in terms of alive node identification, dead node identification and network life time. The comparison result shows that DEE outperforms the existing algorithms.

Keywords

Wireless sensor network Cuckoo search Energy efficiency Fault tolerance 

References

  1. 1.
    Kuila, P., Jana, P.K.: Energy efficient load-balanced clustering algorithm for wireless senso networks. Procedia Technol. 6, 771–777 (2012)CrossRefGoogle Scholar
  2. 2.
    Azharuddin, M., Jana, P.K.: PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Comput. 21(22), 6825–6839 (2017)CrossRefGoogle Scholar
  3. 3.
    Ghiasiana, A., Hosivandib, M.: Cuckoo based clustering algorithm for wireless sensor network. Int. J. Comput. (IJC) 27(1), 146–158 (2017)Google Scholar
  4. 4.
    Mannan, M., Rana, S.B.: Fault tolerance in wireless sensor network. Int. J. Curr. Eng. Tech. 5(3) (2015). E-ISSN 2277-4106, P-ISSN 2347-5161Google Scholar
  5. 5.
    Singh, S.S., Jinila, Y.B.: Sensor node failure detection using checkpoint recovery algorithm. In: Fifth International Conference on Recent Trends in Information Technology (2016)Google Scholar
  6. 6.
    Kuila, P., Jana, P.K.: A novel differential evolution-based clustering algorithm for wireless sensor networks. Appl. Soft Comput. 25, 414–425 (2014)CrossRefGoogle Scholar
  7. 7.
    Sharawi, M., Emary, E., Saroit, I.A., El-Mahdy, H.: Bat swarm algorithm for wireless sensor networks lifetime optimization. Int. J. Sci. Res. (IJSR) 3(5), 654–664 (2014)Google Scholar
  8. 8.
    Yang, X.-S.: Nature-Inspired Optimization Algorithm. Elsevier Inc., Amsterdam (2014)zbMATHGoogle Scholar
  9. 9.
    Yang, X.-S., Deb, S.: Engineering optimisation by cuckoo Search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)zbMATHGoogle Scholar
  10. 10.
    Kaur, S., Mahajan, R.: Hybrid meta-heuristic optimization-based energy efficient protocol for wireless sensor networks. Egypt. Inform. J. 19(3), 145–150 (2018)CrossRefGoogle Scholar
  11. 11.
    Azharuddin, M., Kuila, P., Jana, P.K.: A distributed fault-tolerant clustering algorithm for wireless sensor networks. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 997–1002. IEEE (2013)Google Scholar
  12. 12.
    Bhatti, G.K., Raina, J.P.S.: Cuckoo based energy effective routing in wireless sensor network. Int. J. Comput. Sci. Commun. Eng. 3(1), 92–95 (2014)Google Scholar
  13. 13.
    Yang, X.-S., Deb, S.: Cuckoo search via levy flights. In: Proceeding of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publications, USA (2009)Google Scholar
  14. 14.
    Joshi, A.S., Kulkarni, O., Kakandikar, G.M., Nandedkar, V.M.: Cuckoo search optimization- a review. In: ICAAMM, vol. 4, no. 8, pp. 7262–7269 (2016)Google Scholar
  15. 15.
    Das, S., Barani, S., Wagh, S., Sonavane, S.S.: Optimal clustering and routing for wireless sensor network based on cuckoo search. Int. J. Adv. Smart Sens. Netw. Syst. (IJASSN) 7(2/3), 1–13 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceBharathiar UniversityCoimbatoreIndia

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