Skip to main content
Log in

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The previously reported works e.g., E-LEACH, and RCH-LEACH have proposed energy-efficient CH selection methods with coverage enhancement and varying numbers of overall CH nodes in a network. To further improve the performance, the stable CH selection method, i.e., SCH-LEACH is proposed with unexpended energy of nodes. The SCH-LEACH improves the overall lifetime of the network with minimal energy consumption. However, SCH-LEACH compromises the overall throughput of the network. The throughput analysis shows that the throughput of SCH-LEACH is lower than LEACH, E-LEACH, and RCH-LEACH. Therefore, in the proposed work, the extension of the optimal stable number of cluster head selection method is proposed, i.e., OSCH-LEACH, for reliable data transmission and a maximal lifetime of the network. The proposed work analyzes the Kopt selection for the optimal selection of CHs. The simulation results prove the superiority of the proposed protocol in terms of throughput, lifetime, FND, and LND.

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.

Algorithm
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Tsai T, Huang C, Chang C, Hussain MA. Design of wireless vision sensor network for smart home. IEEE Access. 2020;8:60455–67.

    Article  Google Scholar 

  2. Mabrouki J, Azrour M, Dhiba D, Farhaoui Y, Hajjaji SE. IoT based data logger for weather monitoring using Arduino-based wireless sensor networks with remote graphical application and alerts. Big Data Min Anal. 2021;4(1):25–32.

    Article  Google Scholar 

  3. Xu F, Ye H, Yang F, Zhao C. Software defined mission-critical wireless sensor network: architecture and edge offloading strategy. IEEE Access. 2019;7:10383–91.

    Article  Google Scholar 

  4. Behera TM, Mohapatra SK, Samal UC, Khan MS, Daneshmand M, Gandomi AH. Residual energy-based cluster-head selection in WSNs for IoT application. IEEE Int Things J. 2019;6(3):5132–9.

    Article  Google Scholar 

  5. Clare L, Pottie G, Agre J. “Self-organizing distributed sensor networks,” Proc. SPIE Conf. Unattended Ground Sensor Technologies and Applications,” 1999;229–237.

  6. Mody S, Mirkar S, Ghag R, Kotecha P. “Cluster Head Selection Algorithm For Wireless Sensor Networks Using Machine Learning,” 2021 International Conference on Computational Performance Evaluation (ComPE), Shillong, India, 2021; 445–450. https://doi.org/10.1109/ComPE53109.2021.9752264.

  7. Heinzelman WR, Chandrakasan A, Balakrishnan H. “Energy efficient communication protocol for wireless microsensor networks,” Proc. of 33rd Annual Hawaii Inter. Conf. on Syst. Sci. 2000;1–10.

  8. Leu J, Chiang T, Yu M, Su K. Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. IEEE Commun Lett. 2015;19(2):259–62.

    Article  Google Scholar 

  9. Younis O, Fahmy S. HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput. 2004;3(4):366–79.

    Article  Google Scholar 

  10. Panchal A, Singh RK. “RCH-LEACH: residual energy based cluster head selection in LEACH for wireless sensor networks,” 2020 International Conference on Electrical and Electronics Engineering (ICE3). 2020;322–325.

  11. Ding P, Holliday J, Celik A. “Distributed energy-efficient hierarchichal clustering for wireless sensor networks,” Proc. 1st IEEE Int. Conf. Distrib. Comput. Sensor Syst. (DCOSS). 2005;322–339.

  12. Saranya V, Shankar S, Kanagachidambaresan GR. Energy efficient clustering scheme (EECS) for wireless sensor network with mobile sink. Wireless Pers Commun. 2018;100:1553–67.

    Article  Google Scholar 

  13. Zhixiang D, Bensheng Q. “Three-layered routing protocol for WSN based on LEACH algorithm,” IET Conf. on Wireless, Mobile and Sens. Netw. (CCWMSN07). 2007;72–75

  14. Handy MJ, Haase M, Timmermann D. “Low energy adaptive clustering hierarchy with deterministic cluster-head selection,” 4th Inter. Workshop on Mobile and Wireless Commun. Netw., 2002;368–372.

  15. Lotf JJ, Bonab MN, Khorsandi S. “A Novel cluster-based routing protocol with extending lifetime for wireless sensor networks,” 5th IFIP International Conference on Wireless and Optical Communications Networks 2008;1–5.

  16. Sharawi M, Emary E. “Impact of grey wolf optimization on WSN cluster formation and lifetime expansion,” 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), Doha, Qatar, 2017;157–162. https://doi.org/10.1109/ICACI.2017.7974501.

  17. Patidar Y, Jain M, Vyas AK. “Modified unexpended energy based stable cluster head selection for homogeneous wireless sensor networks (WSNs),” 2022 3rd International Conference on Computing, Analytics and Networks (ICAN), Rajpura, Punjab, India, 2022;1–5. https://doi.org/10.1109/ICAN56228.2022.10007098.

  18. Hoang DC, Kumar R, Panda SK. “Fuzzy C-means clustering protocol for wireless sensor networks,” IEEE International Symposium on Industrial Electronics. 2010;3477–3482.

  19. Zagrouba R, Kardi A. Comparative study of energy efficient routing techniques in wireless sensor networks. Information. 2021. https://doi.org/10.3390/info12010042.

    Article  Google Scholar 

  20. Elhoseny M, Rajan RS, Hammoudeh M, Shankar K, Aldabbas O. Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks. Int J Distrib Sens Netw. 2020. https://doi.org/10.1177/1550147720949133.

    Article  Google Scholar 

  21. Agbehadji IE et al. “Bio-inspired energy efficient clustering approach for wireless sensor networks,” 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco, 2019;1–8. https://doi.org/10.1109/WINCOM47513.2019.8942532.

  22. Yousif YK, Badlishah R, Yaakob N, et al. An energy efficient and load balancing clustering scheme for wireless sensor network (WSN) based on distributed approach. J Phys Conf Ser. 2018. https://doi.org/10.1088/1742-6596/1019/1/012007.

    Article  Google Scholar 

  23. Abbasi AA, Younis M. A survey on clustering algorithms for wireless sensor networks. Comput Commun. 2007;30(14–15):2826–41. https://doi.org/10.1016/j.comcom.2007.05.024. (ISSN 0140-3664).

    Article  Google Scholar 

  24. Kumar P, Dwivedi R, Tyagi V. “Fuzzy ant colony optimization based energy efficient routing for mixed wireless sensor network,” 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, 2019;1–7. https://doi.org/10.1109/ICICT46931.2019.8977699.

  25. Vellaichamy J, Basheer S, Bai PSM, Khan M, Kumar Mathivanan S, Jayagopal P, Babu JC. Wireless sensor networks based on multi-criteria clustering and optimal bio-inspired algorithm for energy-efficient routing. Appl Sci. 2023;13(5):2801. https://doi.org/10.3390/app13052801.

    Article  Google Scholar 

  26. Mirjalili S. SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst. 2016;96:120–33. https://doi.org/10.1016/j.knosys.2015.12.022. (ISSN 0950–7051).

    Article  Google Scholar 

  27. Seyyedabbasi A. WOASCALF: a new hybrid whale optimization algorithm based on sine cosine algorithm and levy flight to solve global optimization problems. Adv Eng Softw. 2022. https://doi.org/10.1016/j.advengsoft.2022.103272. (ISSN 0965–9978).

    Article  Google Scholar 

  28. Guo X, Ye Y, Li L, Wu R, Sun X. WSN clustering routing algorithm combining sine cosine algorithm and Lévy MUTATION. IEEE Access. 2023;11:22654–63. https://doi.org/10.1109/ACCESS.2023.3252027.

    Article  Google Scholar 

  29. Patidar Y, Jain M, Vyas AK. “Comparative analysis of clustering and routing protocols for energy-efficient WSN,” 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2022;1768–1773. https://doi.org/10.1109/ICAC3N56670.2022.10074074.

Download references

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yogesh Patidar.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Research Involves Human/Animal Participants

This article does not contain any studies with human/animal participants performed by any of the authors.

Additional information

Publisher's Note

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

This article is part of the topical collection “Diverse Applications in Computing, Analytics and Networks” guest edited by Archana Mantri and Sagar Juneja.

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

Patidar, Y., Jain, M. & Vyas, A.K. Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network. SN COMPUT. SCI. 5, 218 (2024). https://doi.org/10.1007/s42979-023-02459-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02459-9

Keywords

Navigation