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Hybrid Mutualism Mechanism-Inspired Butterfly and Flower Pollination Optimization Algorithm for Lifetime Improving Energy‐Efficient Cluster Head Selection in WSNs

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Abstract

Rapid advancement of technologies in Wireless Sensor Networks is attracting maximized attention across the scientific community due to its suitability and diversified coverage in real life applications. WSNs due to their features of resource limitation and infrastructure-less deployment introduces the most challenging issues of network lifetime improvement, energy stability and reliable cluster head selection, which is still a herculean task. Clustering is an indispensable mechanism employed for selecting an optimal cluster head with the objective of extending network lifetime with energy stability that achieves efficient data transmission. Cluster head selection through meta-heuristic algorithms introduces the merits of simplicity, flexibility, derivation free and prevents local optima. In this paper, Hybrid Mutualism Mechanism-inspired Butterfly and Flower Pollination Optimization Algorithm (HMMB-FPOA) is proposed for energy‐efficient cluster head selection that attributes towards better energy stability and sustained network lifetime. Mutualism phase includes the symbiosis organisms search over flower pollination optimization algorithm for incorporating strong exploitation capability into butterfly optimization algorithm that prevents losses of premature convergence as it may lose its diversity. This integration of FPOA and BOA improves the overall exploration ability to the expected level, such that convergence speed of the algorithms is accelerated. It also balances the capabilities of exploitation and exploration by dynamically increasing the adaptive switching probability that results in prolonged network lifetime. The simulation results of HMMB-FPOA confirmed an enhanced performance of the network in terms of alive nodes, dead nodes, residual energy, overall throughput, and convergence rate on par with the existing competitive cluster head selection algorithms.

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References

  1. Umbreen, S., Shehzad, D., Shafi, N., Khan, B., & Habib, U. (2020). An energy-efficient mobility-based cluster head selection for lifetime enhancement of wireless sensor networks. IEEE Access, 8(2), 207779–207793.

    Article  Google Scholar 

  2. Jia, D., Zhu, H., Zou, S., & Hu, P. (2016). Dynamic cluster head selection method for wireless sensor network. IEEE Sensors Journal, 16(8), 2746–2754. https://doi.org/10.1109/jsen.2015.2512322

    Article  Google Scholar 

  3. Murugaanandam, S., & Ganapathy, V. (2019). Reliability-based cluster head selection methodology using fuzzy logic for performance improvement in WSNs. IEEE Access, 7(2), 87357–87368. https://doi.org/10.1109/access.2019.2923924

    Article  Google Scholar 

  4. El Alami, H., & Najid, A. (2019). ECH: An enhanced clustering hierarchy approach to maximize lifetime of wireless sensor networks. IEEE Access, 7(2), 107142–107153. https://doi.org/10.1109/access.2019.2933052

    Article  Google Scholar 

  5. Wang, C., & Wang, S. (2019). Research on uneven clustering APTEEN in CWSN based on ant colony algorithm. IEEE Access, 7(2), 163654–163664. https://doi.org/10.1109/access.2019.2950855

    Article  Google Scholar 

  6. Zhu, B., Bedeer, E., Nguyen, H. H., Barton, R., & Henry, J. (2021). Improved Soft-K-Means clustering algorithm for balancing energy consumption in wireless sensor networks. IEEE Internet of Things Journal, 8(6), 4868–4881. https://doi.org/10.1109/jiot.2020.3031272

    Article  Google Scholar 

  7. Gautam, N., & Pyun, J. (2010). Distance aware intelligent clustering protocol for wireless sensor networks. Journal of Communications and Networks, 12(2), 122–129. https://doi.org/10.1109/jcn.2010.6391368

    Article  Google Scholar 

  8. Jain, K., & Kumar, A. (2019). An optimal RSSI-based cluster-head selection for sensor networks. International Journal of Adaptive and Innovative Systems, 2(4), 349. https://doi.org/10.1504/ijais.2019.10030287

    Article  Google Scholar 

  9. Baskaran, M., & Sadagopan, C. (2015). Synchronous firefly algorithm for cluster head selection in WSN. The Scientific World Journal, 2015, 1–7. https://doi.org/10.1155/2015/780879

    Article  Google Scholar 

  10. Farman, H., Javed, H., Jan, B., Ahmad, J., Ali, S., Khalil, F. N., & Khan, M. (2017). Analytical network process based optimum cluster head selection in wireless sensor network. PLoS ONE, 12(7), e0180848. https://doi.org/10.1371/journal.pone.0180848

    Article  Google Scholar 

  11. Janakiraman, S., & Priya, M. D. (2020). An energy-proficient clustering-inspired routing protocol using improved bkd-tree for enhanced node stability and network lifetime in wireless sensor networks. International Journal of Communication Systems. https://doi.org/10.1002/dac.4575

    Article  Google Scholar 

  12. John, J., & Rodrigues, P. (2019). A survey of energy-aware cluster head selection techniques in wireless sensor network. Evolutionary Intelligence, 2(2), 45–56. https://doi.org/10.1007/s12065-019-00308-4

    Article  Google Scholar 

  13. Dattatraya, K. N., & Rao, K. R. (2019). Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. Journal of King Saud University - Computer and Information Sciences, 2(1), 45–59. https://doi.org/10.1016/j.jksuci.2019.04.003

    Article  Google Scholar 

  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(2), 1–10. https://doi.org/10.1016/j.swevo.2016.03.003

    Article  Google Scholar 

  15. Li, X., Keegan, B., & Mtenzi, F. (2018). Energy efficient hybrid routing protocol based on the artificial fish swarm algorithm and ant colony optimisation for WSNs. Sensors, 18(10), 3351. https://doi.org/10.3390/s18103351

    Article  Google Scholar 

  16. Rajagopal, A. (2018). Performance analysis for efficient cluster head selection in wireless sensor network using RBFO and hybrid BFO-BSO. International Journal of Wireless Communications and Mobile Computing, 6(1), 89–99.

    Article  Google Scholar 

  17. Rajaram, V., & Kumaratharan, N. (2020). An optimized clustering using hybrid meta-heuristic approach for wireless sensor networks. International Journal of Communication Systems, 33(18).

  18. Han, Y., Li, G., Xu, R., Su, J., Li, J., & Wen, G. (2020). Clustering the wireless sensor networks: A meta-heuristic approach. IEEE Access, 8(2), 214551–214564. https://doi.org/10.1109/access.2020.3041118

    Article  Google Scholar 

  19. Murugadass, G., & Sivakumar, P. (2020). A hybrid elephant herding optimization and cultural algorithm for energy-balanced cluster head selection scheme to extend the lifetime in WSNs. International Journal of Communication Systems, 3(2), e4538.

    Article  Google Scholar 

  20. Tamilarasan, N., Lenin, S., Jayapandian, N., & Subramanian, P. (2021). Hybrid shuffled frog leaping and improved biogeography-based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks. International Journal of Communication Systems, 34(4), 67–79.

    Article  Google Scholar 

  21. Nagarajan, L., & Thangavelu, S. (2020). Hybrid grey wolf sunflower optimisation algorithm for energy-efficient cluster head selection in wireless sensor networks for lifetime enhancement. IET Communications, 15(3), 384–396.

    Article  Google Scholar 

  22. Wang, Z., Luo, Q., & Zhou, Y. (2020). Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Engineering with Computers, 3(1), 45–56.

    Google Scholar 

  23. Gálvez, J., Cuevas, E., & Avalos, O. (2017). Flower pollination algorithm for multimodal optimization. International Journal of Computational Intelligence Systems, 10(1), 627. https://doi.org/10.2991/ijcis.2017.10.1.42

    Article  Google Scholar 

  24. Sharma, S., & Saha, A. K. (2019). M-MBOA: A novel butterfly optimization algorithm enhanced with mutualism scheme. Soft Computing, 24(7), 4809–4827. https://doi.org/10.1007/s00500-019-04234-6

    Article  Google Scholar 

  25. Sahoo, B. M., Pandey, H. M., & Amgoth, T. (2021). GAPSO-H: A hybrid approach towards optimizing the cluster-based routing in wireless sensor network. Swarm and Evolutionary Computation, 60(3), 100772.

    Article  Google Scholar 

  26. Balamurugan, A., Priya, M. D., Janakiraman, S., & Christy, J. (2021). Hybrid stochastic ranking and opposite differential evolution-based enhanced firefly optimization algorithm for extending network lifetime through efficient clustering in WSNs. J Network System Management, 29, 1–31.

    Article  Google Scholar 

  27. Balamurugan, A., Janakiraman, S., Priya, M. D., & Malar, A. C. (2022). Hybrid marine predators optimization and improved particle swarm optimization-based optimal cluster routing in wireless sensor networks (WSNs). China Communications, 19(6), 219–247.

    Article  Google Scholar 

  28. Sengathir, J., Rajesh, A., Dhiman, G., Vimal, S., Yogaraja, C., & Viriyasitavat, W. (2021). A novel cluster head selection using hybrid artificial bee colony and firefly algorithm for network lifetime and stability in WSNs. Connection Science, 34(1), 387–408.

    Article  Google Scholar 

  29. Bandi, R., Ananthula, V. R., & Janakiraman, S. (2021). Self-adapting differential search strategies improved artificial bee colony algorithm-based cluster head selection scheme for WSNs. Wireless Personal Communications, 121(3), 2251–2272.

    Article  Google Scholar 

  30. Ali, H., Tariq, U. U., Hussain, M., Lu, L., Panneerselvam, J., & Zhai, X. (2021). ARSH-FATI: A novel Metaheuristic for cluster head selection in wireless sensor networks. IEEE Systems Journal, 15(2), 2386–2397.

    Article  Google Scholar 

  31. Panchal, A., & Singh, R. K. (2021). EOCGS: Energy efficient optimum number of cluster head and grid head selection in wireless sensor networks. Telecommunication Systems, 3(4), 1–13.

    Article  Google Scholar 

  32. Subramanian, P., Sahayaraj, J. M., Senthilkumar, S., & Alex, D. S. (2020). A hybrid grey wolf and crow search optimization algorithm-based optimal cluster head selection scheme for wireless sensor networks. Wireless Personal Communications, 113(2), 905–925.

    Article  Google Scholar 

  33. Guleria, K., & Verma, A. K. (2019). Meta-heuristic ant colony optimization based unequal clustering for wireless sensor network. Wireless Personal Communications, 105(3), 891–911.

    Article  Google Scholar 

  34. Arora, S., & Singh, S. (2017). A hybrid optimisation algorithm based on butterfly optimisation algorithm and differential evolution. International Journal of Swarm Intelligence, 3(2–3), 152–169.

    Article  Google Scholar 

  35. Arora, S., & Singh, S. (2017). An effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization.

  36. Sharma, S., & Saha, A. K. (2020). m-MBOA: A novel butterfly optimization algorithm enhanced with mutualism scheme. Soft Computing, 24(7), 4809–4827.

    Article  Google Scholar 

  37. Rambabu, B., Venugopal Reddy, A., & Janakiraman, S. (2019). Hybrid artificial bee colony and monarchy butterfly optimization algorithm (HABC-MBOA)-based cluster head selection for WSNs. Journal of King Saud University - Computer and Information Sciences, 2(2), 45–56.

    Google Scholar 

  38. Janakiraman, S. (2018). A hybrid ant colony and artificial bee colony optimization algorithm-based cluster head selection for IoT. Procedia Computer Science, 143, 360–366.

    Article  Google Scholar 

  39. Janakiraman, S., Priya, M., Devi, S., Sandhya, G., Nivedhitha, G., & Padmavathi, S. (2018). A Markov process-based opportunistic trust factor estimation mechanism for efficient cluster head selection and extending the lifetime of wireless sensor networks. EAI Endorsed Transactions on Energy Web, 2(1), 168093.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of CSE, Annamalai University, Chidambaram, Tamil Nadu, 608 002, India

    S. Jaya Pratha & V. Asanambigai

  2. Department of CSE, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, 641 048, India

    S. R. Mugunthan

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  1. S. Jaya Pratha
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Correspondence to S. Jaya Pratha.

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Pratha, S.J., Asanambigai, V. & Mugunthan, S.R. Hybrid Mutualism Mechanism-Inspired Butterfly and Flower Pollination Optimization Algorithm for Lifetime Improving Energy‐Efficient Cluster Head Selection in WSNs. Wireless Pers Commun 128, 1567–1601 (2023). https://doi.org/10.1007/s11277-022-10010-x

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