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Wireless Personal Communications

, Volume 104, Issue 2, pp 677–694 | Cite as

Moth Flame Optimization Based Energy Efficient Stable Clustered Routing Approach for Wireless Sensor Networks

  • Nitin MittalEmail author
Article
  • 59 Downloads

Abstract

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is an efficient data aggregation method that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, moth flame optimization (MFO) based threshold-sensitive energy-efficient clustering protocol (TECP) is proposed to extend the stability period of the network. Multi-hop communication between CHs and BS is utilized using MFO to achieve optimal link cost for load balancing of distant CHs and energy minimization. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

Keywords

MFO WSN Network lifetime Stability period 

Notes

References

  1. 1.
    Afsar, M. M., & Tayarani-N, M. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226.CrossRefGoogle Scholar
  2. 2.
    Anisi, M. H., Abdul-Salaam, G., Idris, M. Y. I., Wahab, A. W. A., & Ahmedy, I. (2017). Energy harvesting and battery power based routing in wireless sensor networks. Wireless Networks, 23(1), 249–266.CrossRefGoogle Scholar
  3. 3.
    Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications, Surveys and Tutorials, 15(2), 551–591.CrossRefGoogle Scholar
  4. 4.
    Halawani, S., & Khan, A. W. (2010). Sensors lifetime enhancement techniques in wireless sensor networks—A survey. Journal of Computing, 2(5), 34–47.Google Scholar
  5. 5.
    Idris, M. Y. I., Znaid, A. M. A., Wahab, A. W. A., Qabajeh, L. K., & Mahdi, O. A. (2017). Low communication cost (LCC) scheme for localizing mobile wireless sensor networks. Wireless Networks, 23(3), 737–747.CrossRefGoogle Scholar
  6. 6.
    Heinzelman, W. B., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (HICSS-33). IEEE.  https://doi.org/10.1109/hicss.2000.926982.
  7. 7.
    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.CrossRefGoogle Scholar
  8. 8.
    Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In 15th international parallel and distributed processing symposium (IPDPS’01) workshops, USA, California (pp. 2009–2015).Google Scholar
  9. 9.
    Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In International parallel and distributed processing symposium, Florida (pp. 195–202).Google Scholar
  10. 10.
    Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In Proceedings of the international workshop on SANPA. http://open.bu.edu/xmlui/bitstream/handle/2144/1548/2004-022-sep.pdf?sequence=1.
  11. 11.
    Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor network. Computer Communications, 29, 2230–2237.  https://doi.org/10.1016/j.comcom.2006.02.017.CrossRefGoogle Scholar
  12. 12.
    Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.  https://doi.org/10.1109/LCOMM.2012.073112.120450.CrossRefGoogle Scholar
  13. 13.
    Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32, 662–667.  https://doi.org/10.1016/j.comcom.2008.11.025.CrossRefGoogle Scholar
  14. 14.
    Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems, 4(1), 9–16.  https://doi.org/10.1049/iet-wss.2012.0150.Google Scholar
  15. 15.
    Tarhani, M., Kavian, Y. S., & Siavoshi, S. (2014). SEECH: Scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sensors Journal, 14(11), 3944–3954.  https://doi.org/10.1109/JSEN.2014.2358567.CrossRefGoogle Scholar
  16. 16.
    Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2011). A deterministic energy-efficient clustering protocol for wireless sensor networks. In Proceedings of the 7th international conference on intelligent sensors, sensor networks and information processing (ISSNIP ‘11) (pp 341–346). IEEE.  https://doi.org/10.1109/issnip.2011.6146592.
  17. 17.
    Mittal, N., & Singh, U. (2015). Distance-based residual energy-efficient stable election protocol for WSNs. Arabian Journal of Science and Engineering, 40(6), 1637–1646.  https://doi.org/10.1007/s13369-015-1641-x.CrossRefGoogle Scholar
  18. 18.
    Mittal, N., Singh, U., & Sohi, B. S. (2016). A stable energy efficient clustering protocol for wireless sensor networks. Wireless Networks.  https://doi.org/10.1007/s11276-016-1255-6.Google Scholar
  19. 19.
    Adnan, Md A, Razzaque, M. A., Ahmed, I., & Isnin, I. F. (2014). Bio-mimic optimization strategies in wireless sensor networks: A survey. Sensors, 14, 299–345.  https://doi.org/10.3390/s140100299.CrossRefGoogle Scholar
  20. 20.
    Hussain, S., & Matin, A. W. (2006). Hierarchical cluster-based routing in wireless sensor networks. In IEEE/ACM international conference on information processing in sensor networks, IPSN.Google Scholar
  21. 21.
    Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation.  https://doi.org/10.1016/j.swevo.2011.06.004.Google Scholar
  22. 22.
    Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12, 1950–1957.  https://doi.org/10.1016/j.asoc.2011.04.007.CrossRefGoogle Scholar
  23. 23.
    Khalil, E. A., & Attea, B. A. (2013). Stable-aware evolutionary routing protocol for wireless sensor networks. Wireless Personal Communications, 69(4), 1799–1817.CrossRefGoogle Scholar
  24. 24.
    Mittal, N., Singh, U., & Sohi, B. S. (2017). A novel energy efficient stable clustering approach for wireless sensor networks. Wireless Personal Communications, 95(3), 2947–2971.CrossRefGoogle Scholar
  25. 25.
    Mittal, N., Singh, U., & Sohi, B. S. (2017). Harmony search algorithm based threshold-sensitive energy-efficient clustering protocols for WSNs. Ad Hoc and Sensor Wireless Networks, 36(1–4), 149–174.Google Scholar
  26. 26.
    Mittal, N., Singh, U., & Sohi, B. S. (2018). A boolean spider monkey optimization based energy efficient clustering approach for WSNs. Wireless Networks, 24(6), 2093–2109.CrossRefGoogle Scholar
  27. 27.
    Mittal, N., Singh, U., & Sohi, B. S. (2016). Modified grey wolf optimizer for global engineering optimization. Applied Computational Intelligence and Soft Computing, 2016, 1–16.CrossRefGoogle Scholar
  28. 28.
    Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.CrossRefGoogle Scholar
  29. 29.
    Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.CrossRefGoogle Scholar
  30. 30.
    Shokouhifar, M., & Jalali, A. (2015). A new evolutionary based application specific routing protocol for clustered wireless sensor networks. International Journal of Electronics and Communications, 69, 432–441.CrossRefGoogle Scholar
  31. 31.
    Rao, P. C., & Banka, H. (2015). Energy efficient clustering algorithms for wireless sensor networks: Novel chemical reaction optimization approach. Wireless Networks.  https://doi.org/10.1007/s11276-015-1156-0.Google Scholar
  32. 32.
    Rao, P. C., & Banka, H. (2016). Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks. Wireless Networks.  https://doi.org/10.1007/s11276-015-1148-0.Google Scholar
  33. 33.
    Rao, P. C., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005–2020.CrossRefGoogle Scholar
  34. 34.
    Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.CrossRefGoogle Scholar
  35. 35.
    Mittal, N., Singh, U., & Sohi, B. S. (2018). An energy aware cluster-based stable protocol for wireless sensor networks. Neural Computing and Applications.  https://doi.org/10.1007/s00521-018-3542-x.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringChandigarh UniversityMohaliIndia

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