Skip to main content

Advertisement

Log in

An Energy Efficient Wireless Sensor Network with Flamingo Search Algorithm Based Cluster Head Selection

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSN) are emerging versatile and low-cost solutions for several applications. However, energy efficiency is a major issue in WSNs. The sensor nodes typically have limited energy but the energy consumption exceeds during data transmission. An energy efficient cluster based routing protocol reduces the transmission distance among the base station (BS) and the sensor nodes in terms of organizing the nodes in the form of clusters and evade the nodes with lower energy. Therefore, energy efficient Ultra-Scalable Ensemble Clustering technique is introduced in this work to cluster the nodes for handling large data. Then, the Flamingo Search Algorithm is employed for cluster head (CH) selection due to its less computational complexity and high stability. Finally, Q-Learning approach is adopted to select the shortest path between CHs and BS as it is capable of path selection at complex network conditions. The reward points in this approach are generated based on the objective function that considers the distance among the CH and BS, coverage area and energy consumption. Experiments are evaluated and analyzed with existing approaches in terms of alive nodes, time consumption, rounds for last node dead, first node dead, half node dead, throughput and total residual energy. The consequences prove that the offered technique can enhance the energy efficiency of WSN compared to similar existing approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

Data will be shared on the reasonable request.

References

  1. Yun, W. K., & Yoo, S. J. (2021). Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access, 9, 10737–10750. https://doi.org/10.1109/ACCESS.2021.3051360

    Article  Google Scholar 

  2. Jin, Y., Kwak, K. S., & Yoo, S. J. (2020). A novel energy supply strategy for stable sensor data delivery in wireless sensor networks. IEEE Systems Journal, 1–12. https://doi.org/10.1109/jsyst.2019.2963695.

  3. Maheshwari, P., Sharma, A. K., & Verma, K. (2020). Energy efficient cluster based Routing protocol for WSN using Butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 102317. https://doi.org/10.1016/j.adhoc.2020.102317.

  4. Wang, Z., Ding, H., Li, B., Bao, L., & Yang, Z. (2020). An energy efficient routing protocol based on Improved Artificial Bee colony algorithm for Wireless Sensor Networks. Ieee Access: Practical Innovations, Open Solutions, 1–1. https://doi.org/10.1109/access.2020.3010313.

  5. Mansourkiaie, F., & Ahmed, M. H. (2015). Cooperative routing in wireless networks: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 17(2), 604–626. https://doi.org/10.1109/comst.2014.2386799.

    Article  Google Scholar 

  6. Haseeb, K., Islam, N., Almogren, A., Din, I. U., Almajed, H. N., & Guizani, N. (2019). Secret sharing-based Energy-aware and multi-hop routing protocol for IoT based WSNs. Ieee Access : Practical Innovations, Open Solutions, 1–1. https://doi.org/10.1109/access.2019.2922971.

  7. Mazaideh, M. A., & Levendovszky, J. (2021). A multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm. Journal of Communications and Networks, 23(2), 138–147. https://doi.org/10.23919/jcn.2021.000003.

    Article  Google Scholar 

  8. Adnan, M., Yang, L., Ahmad, T., & Tao, Y. (2021). An unequally clustered multi-hop routing protocol based on fuzzy logic for Wireless Sensor Networks. Ieee Access : Practical Innovations, Open Solutions, 9, 38531–38545. https://doi.org/10.1109/access.2021.3063097.

    Article  Google Scholar 

  9. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (n.d.) (Eds.). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/hicss.2000.926982.

  10. SureshKumar, K., & Vimala, P. (2021). Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks. Computer Networks, 197, 108250.

    Article  Google Scholar 

  11. Maheshwari, P., Sharma, A. K., & Verma, K. (2021). Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Networks, 110, 102317.

    Article  Google Scholar 

  12. Al-Otaibi, S., Al-Rasheed, A., Mansour, R. F., Yang, E., Joshi, G. P., & Cho, W. (2021). Hybridization of metaheuristic algorithm for dynamic cluster-based routing protocol in wireless sensor Networksx. Ieee Access: Practical Innovations, Open Solutions, 9, 83751–83761.

    Article  Google Scholar 

  13. Zachariah, U. E., & Kuppusamy, L. (2022). A hybrid approach to energy efficient clustering and routing in wireless sensor networks. Evolutionary Intelligence, 15(1), 593–605.

    Article  Google Scholar 

  14. Nandan, A. S., Singh, S., & Awasthi, L. K. (2021). An efficient cluster head election based on optimized genetic algorithm for movable sinks in IoT enabled HWSNs. Applied Soft Computing, 107, 107318.

    Article  Google Scholar 

  15. Alazab, M., Lakshmanna, K., Reddy, T., Pham, Q. V., & Maddikunta, P. K. R. (2021). Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities. Sustainable Energy Technologies and Assessments, 43, 100973.

    Article  Google Scholar 

  16. Xu, X. W., Pan, J. S., Mohamed, A. W., & Chu, S. C. (2022). Improved fish migration optimization with the opposition learning based on elimination principle for cluster head selection. Wireless Networks, 28(3), 1017–1038.

    Article  Google Scholar 

  17. Yadav, R. K., & Mahapatra, R. P. (2022). Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network. Pervasive and Mobile Computing, 79, 101504.

    Article  Google Scholar 

  18. Kathiroli, P., & Selvadurai, K. (2022). Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks. Journal of King Saud University-Computer and Information Sciences, 34(10), 8564–8575.

    Article  Google Scholar 

  19. Sengathir, J., Rajesh, A., Dhiman, G., Vimal, S., Yogaraja, C. A., & Viriyasitavat, W. (2022). 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 

  20. Narayan, V., Daniel, A. K., & Chaturvedi, P. (2022). FGWOA: An efficient heuristic for cluster head selection in WSN using fuzzy based grey wolf optimization algorithm.

  21. Sheriba, S. T., & Rajesh, D. H. (2021). Energy-efficient clustering protocol for WSN based on improved black widow optimization and fuzzy logic. Telecommunication Systems, 77(1), 213–230.

    Article  Google Scholar 

  22. Yadav, R. K., & Mahapatra, R. P. (2021). Energy aware optimized clustering for hierarchical routing in wireless sensor network. Computer Science Review, 41, 100417.

    Article  MathSciNet  MATH  Google Scholar 

  23. Osamy, W., El-Sawy, A. A., & Salim, A. (2020). CSOCA: Chicken Swarm optimization based clustering algorithm for Wireless Sensor Networks. Ieee Access : Practical Innovations, Open Solutions, 8, 60676–60688. https://doi.org/10.1109/access.2020.2983483.

    Article  Google Scholar 

  24. Han, Y., Li, G., Xu, R., Su, J., Li, J., & Wen, G. (2020). Clustering the Wireless Sensor Networks: A meta-heuristic approach. Ieee Access : Practical Innovations, Open Solutions, 8, 214551–214564. https://doi.org/10.1109/access.2020.3041118.

    Article  Google Scholar 

  25. Arikumar, K. S., Natarajan, V., & Satapathy, S. C. (2020). EELTM: an energy efficient LifeTime maximization Approach for WSN by PSO and fuzzy-based unequal clustering. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-020-04616-1.

    Article  Google Scholar 

  26. Arunachalam, N., Shanmugasundaram, G., & Arvind, R. (2021). Squirrel search optimization-based cluster head selection technique for prolonging lifetime in WSN’s. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08843-z.

    Article  Google Scholar 

  27. Liang, J., Xu, Z., Xu, Y., Zhou, W., & Li, C. (2021). Adaptive cooperative routing transmission for energy heterogeneous wireless sensor networks. Physical Communication, 49, 101460.

    Article  Google Scholar 

  28. Guo, W., Yan, C., & Lu, T. (2019). Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing. International Journal of Distributed Sensor Networks, 15(2), 155014771983354. https://doi.org/10.1177/1550147719833541.

    Article  Google Scholar 

  29. Younus, M. U., Khan, M. K., & Bhatti, A. R. (2021). Improving the software-defined wireless sensor networks routing performance using reinforcement learning. IEEE Internet of Things Journal, 9(5), 3495–3508.

    Article  Google Scholar 

  30. Huang, D., Wang, C. D., Wu, J., Lai, J. H., & Kwoh, C. K. (2019). Ultra-scalable spectral clustering and ensemble clustering. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2019.2903410.

  31. Zhiheng, W., & Jianhua, L. (2021). Flamingo search algorithm: A new swarm intelligence optimization algorithm. Ieee Access : Practical Innovations, Open Solutions, 9, 88564–88582. https://doi.org/10.1109/access.2021.3090512.

    Article  Google Scholar 

  32. Rezaeipanah, A., Amiri, P., Nazari, H., Mojarad, M., & Parvin, H. (2021). An energy-aware hybrid approach for wireless sensor networks using re-clustering-based multi-hop routing. Wireless Personal Communications, 120(4), 3293–3314.

    Article  Google Scholar 

Download references

Funding

Funding information is not applicable because no funding was received.

Author information

Authors and Affiliations

Authors

Contributions

I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, andh agree to its submission.

Corresponding author

Correspondence to Robin Abraham.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abraham, R., Vadivel, M. An Energy Efficient Wireless Sensor Network with Flamingo Search Algorithm Based Cluster Head Selection. Wireless Pers Commun 130, 1503–1525 (2023). https://doi.org/10.1007/s11277-023-10342-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10342-2

Keywords

Navigation