Hybrid Cluster Head Election for WSN Based on Firefly and Harmony Search Algorithms

  • Anupkumar M. BongaleEmail author
  • C. R. Nirmala
  • Arunkumar M. Bongale


Design of energy efficient routing protocols for Wireless Sensor Network (WSN) is a great challenge for researchers. Recently, WSNs have gained lot of popularity and many energy efficient routing solutions are proposed. Most of the existing routing protocols focus on cluster head election and ignoring other important aspects of routing such cluster formation, data aggregation, etc. This research article presents a hybrid cluster head election for WSN based on firefly and harmony search algorithms. The contributions of the proposed protocols are (1) two level cluster head election strategy. In the first stage harmony search algorithm is used to determine initial set of energy efficient cluster head nodes that are sufficiently separated from on another by certain optimal distance. Then tentatively elected cluster head nodes are refined by firefly algorithm by considering the parameters such as node density, cluster compactness and energy to be consumed. Sometimes nature inspired optimization techniques may end up in early convergence and to avoid such problems, cluster head election scheme is divided at two levels. (2) a refined cluster formation strategy is designed where a normal node has privilege of joining to cluster head node either based on distance based metric or based on residual energy of cluster heads. This process of cluster formation helps in reduced energy consumption. The presented protocol is compared with some of the well-known clustering protocols such as LEACH, LEACH-C, EOICHD, and simple firefly based routing protocol based on the evaluation metrics such as number of alive nodes, energy consumption of network, number of packets received by Base Station, First Node Dead, Half Node Dead and Last Node Dead. Implementation is carried out using Network Simulator (NS 2.34) and results show that proposed hybrid cluster head election scheme outperforms the mentioned routing protocols.


Cluster formation Cluster head election Energy efficiency Firefly algorithm Harmony search algrotihm Wireless Sensor Network 



  1. 1.
    Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. New York: Wiley-Interscience.CrossRefGoogle Scholar
  2. 2.
    Zheng, J., & Jamalipour, A. (2009). Wireless sensor networks: A networking perspective. New York: Wiley-IEEE Press.CrossRefGoogle Scholar
  3. 3.
    Kemal, A., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.CrossRefGoogle Scholar
  4. 4.
    Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys Tutorials, 15(2), 551–591.CrossRefGoogle Scholar
  5. 5.
    Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  6. 6.
    Lotf, J. J., Hosseinzadeh, M., Alguliev, R. M. (2010). Hierarchical routing in wireless sensor networks: A survey. In 2010 2nd international conference on computer engineering and technology, (vol. 3, pp. V3–650–V3–654).
  7. 7.
    Singh, S. K., Kumar, P., & Singh, J. P. (2017). A survey on successors of LEACH protocol. IEEE Access, 5, 4298–4328.CrossRefGoogle Scholar
  8. 8.
    Guo, W., & Zhang, W. (2014). A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 38, 185–201.CrossRefGoogle Scholar
  9. 9.
    Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104–122.CrossRefGoogle Scholar
  10. 10.
    Sharma, V., & Pughat, A. (2017). Energy-efficient wireless sensor networks. Boca Raton, FL: CRC Press, Taylor and Francis Group.CrossRefGoogle Scholar
  11. 11.
    Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. simulation, 76(2), 60–68.CrossRefGoogle Scholar
  12. 12.
    Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In Proceedings of the 5th international conference on stochastic algorithms: Foundations and applications, SAGA’09 (pp. 169–178). Berlin, Heidelberg: Springer.Google Scholar
  13. 13.
    Kaur, H., Prabahakar, G. (2016). An advanced clustering scheme for wireless sensor networks using particle swarm optimization. In 2016 2nd international conference on next generation computing technologies (NGCT), (pp. 387–392).Google Scholar
  14. 14.
    Shankar, T., Shanmugavel, S., & Rajesh, A. (2016). Hybrid hsa and pso algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary Computation, 30, 1–10.CrossRefGoogle Scholar
  15. 15.
    Hacioglu, G., Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Expert System with Applications, 59(C), 86–100.CrossRefGoogle Scholar
  16. 16.
    Rajendra Prasad, D., Naganjaneyulu, P. V., & Satya Prasad, K. (2017). Bio-inspired approach for energy aware cluster head selection in wireless sensor networks (pp. 541–550). Singapore: Springer.Google Scholar
  17. 17.
    Zungeru, A. M., Ang, L. M., & Seng, K. P. (2012). Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison. Journal of Network and Computer Applications, 35(5), 1508–1536. Service Delivery Management in Broadband Networks.CrossRefGoogle Scholar
  18. 18.
    Shamsan Saleh, A. M., Ali, B. M., Rasid, M. F. A., & Ismail, A. (2014). A survey on energy awareness mechanisms in routing protocols for wireless sensor networks using optimization methods. Transactions on Emerging Telecommunications Technologies, 25(12), 1184–1207.CrossRefGoogle Scholar
  19. 19.
    Heinzelman, W. R., Chandrakasan, A., Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences, HICSS ’00, (vol. 8, pp. 8020). Washington, DC: IEEE Computer Society.Google Scholar
  20. 20.
    Manjeshwar, A., Agrawal, D. P. (2001). Teen: Arouting protocol for enhanced efficiency in wireless sensor networks. In Proceedings of the 15th international parallel&Amp; distributed processing symposium, IEEE Computer Society, Washington, DC, USA, IPDPS ’01 (pp 189).Google Scholar
  21. 21.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.CrossRefGoogle Scholar
  22. 22.
    Lindsey, S., Raghavendra, C. S. (2002). Pegasis: Power-efficient gathering in sensor information systems. In Proceedings, IEEE aerospace conference (vol. 3, pp. 3–1125–3–1130 vol. 3).Google Scholar
  23. 23.
    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
  24. 24.
    Saleem, M., Caro, G. A. D., & Farooq, M. (2011). Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Information Sciences, 181(20), 4597–4624. Special Issue on Interpretable Fuzzy Systems.CrossRefGoogle Scholar
  25. 25.
    Zeng, B., & Dong, Y. (2016). An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Applied Soft Computing, 41, 135–147.CrossRefGoogle Scholar
  26. 26.
    Ari, A. A. A., Yenke, B. O., Labraoui, N., Damakoa, I., & Gueroui, A. (2016). A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach. Journal of Network and Computer Applications, 69, 77–97.CrossRefGoogle Scholar
  27. 27.
    Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7), 847–860.CrossRefGoogle Scholar
  28. 28.
    Mann, P. S., & Singh, S. (2017). Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks. Engineering Applications of Artificial Intelligence, 57(Supplement C), 142–152., URL
  29. 29.
    Bhatia, T., Kansal, S., Goel, S., & Verma, A. (2016). A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers and Electrical Engineering, 56(Supplement C), 441–455.CrossRefGoogle Scholar
  30. 30.
    Yang, X., He, X. (2013). Firefly algorithm: Recent advances and applications. CoRR arXiv:1308.3898.
  31. 31.
    Nadeem, A., Shankar, T., Sharma, R. K., Roy, S. K. (2016). An application of firefly algorithm for clustering in wireless sensor networks. In Proceedings of the international conference on recent cognizance in wireless communication and image processing New Delhi: Springer.Google Scholar
  32. 32.
    Lalwani, P., Ganguli, I., Banka, H. (2016). Farw: Firefly algorithm for routing in wireless sensor networks. In 2016 3rd international conference on recent advances in information technology (RAIT), (pp 248–252).Google Scholar
  33. 33.
    Prakash, S. K. L. V. S., Reddy, K. S. R. (2014). Firefly inspired energy aware cluster based tree formation in wsn. In 2014 2nd international conference on information and communication technology (ICoICT), (pp. 356–360).Google Scholar
  34. 34.
    Bongale, A. M., & Nirmala, C. R. (2016). Eoichd: A routing scheme for wireless sensor network based on energy and optimal inter cluster head distance. International Journal of Applied Engineering Research, 11(11), 7256–7266.Google Scholar
  35. 35.
    Singh, B., & Lobiyal, D. K. (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences, 2(1), 13.CrossRefGoogle Scholar
  36. 36.
    Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Anupkumar M. Bongale
    • 1
    Email author
  • C. R. Nirmala
    • 2
  • Arunkumar M. Bongale
    • 3
  1. 1.Department of Information TechnologyD Y Patil College of Engineering AmbiPuneIndia
  2. 2.Department of Computer Science and EngineeringBapuji Institute of Engineering and TechnologyDavangereIndia
  3. 3.Department of Mechanical Engineering, Symbiosis Institute of TechnologySymbiosis International (Deemed University)Lavale, PuneIndia

Personalised recommendations