Skip to main content

Advertisement

Log in

A Metaheuristic Algorithm Based Clustering Protocol for Energy Harvesting in IoT-Enabled WSN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

With the development of micro-electro-mechanical-system, energy harvesting (EH)-enabled sensor nodes may be used in many applications. WSNs without EH-enabled nodes still have limited applicability due to limited battery resources. The introduction of EH-enabled sensor nodes in the network increases costs and reduces performance due to environmental factors. We propose the clustering-based BAT algorithm for energy harvesting (CBA-EH) in IoT-enable wireless sensor networks to maximize the network performance. The Bat Optimization Algorithm is employed to optimize the fitness factors associated with CH selection. These factors include residual energy, intra-cluster distance, inter-cluster distance, and distance between EH-nodes and sink. The utilization of EH-enabled nodes enables us to effectively manage and minimize the network’s operational costs. The simulation results show that the suggested approach significantly improves network stability and operating time compared to current methods. In comparison to the GAOC protocol, simulation findings demonstrate that CBA-EH enhances stability, network lifetime, and throughput by 45%, 42.13%, and 48%, respectively.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

All data generated or analysed during this study are included in this paper.

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Hentati, A., Jaafar, W., Frigon, J. F., & Ajib, W. (2020). Analysis of the interdelivery time in IoT energy harvesting wireless sensor networks. IEEE Internet of Things Journal, 8(6), 4920–4930.

    Article  Google Scholar 

  3. Badi, A., & Mahgoub, I. (2021). ReapIoT: Reliable, energy-aware network protocol for large-scale internet-of-things (IoT) applications. IEEE Internet of Things Journal, 8(17), 13582–13592.

    Article  Google Scholar 

  4. Zhang, P., Xiao, G., & Tan, H. P. (2013). Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors. Computer Networks, 57(14), 2689–2704.

    Article  Google Scholar 

  5. Sahoo, B. M., Amgoth, T., & Pandey, H. M. (2020). Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Networks, 106, 102237.

    Article  Google Scholar 

  6. Sahoo, B. M., Pandey, H. M., & Amgoth, T. (2022). A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks. Swarm and Evolutionary Computation, 75, 101151.

    Article  Google Scholar 

  7. 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, 100772.

    Article  Google Scholar 

  8. Verma, S., Sood, N., & Sharma, A. K. (2019). Genetic algorithm-based optimized cluster head selection for single and multiple data sinks in heterogeneous wireless sensor network. Applied Soft Computing, 85, 105788.

    Article  Google Scholar 

  9. Nayyar, A., & Singh, R. (2017). Ant colony optimization (ACO) based routing protocols for wireless sensor networks (WSN): A survey. International Journal of Advanced Computer Science and Applications, 8(2), 148–155.

    Article  Google Scholar 

  10. Tabibi, S., & Ghaffari, A. (2019). Energy-efficient routing mechanism for mobile sink in wireless sensor networks using particle swarm optimization algorithm. Wireless Personal Communications, 104, 199–216.

    Article  Google Scholar 

  11. Sah, D. K., & Amgoth, T. (2020). A novel efficient clustering protocol for energy harvesting in wireless sensor networks. Wireless Networks, 26(6), 4723–4737.

    Article  Google Scholar 

  12. Wang, T., Zhang, G., Yang, X., & Vajdi, A. (2018). Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. Journal of Systems and Software, 146, 196–214.

    Article  Google Scholar 

  13. Gupta, P., Tripathi, S., Singh, S., & Gupta, V. S. (2023). MPPT-EPO optimized solar energy harvesting for maximizing the WSN lifetime. Peer-to-Peer Networking and Applications, 16(1), 347–357.

    Article  Google Scholar 

  14. Lipare, A., Edla, D. R., & Dharavath, R. (2021). Energy efficient fuzzy clustering and routing using BAT algorithm. Wireless Networks, 27, 2813–2828.

    Article  Google Scholar 

  15. Nandhini, P., & Suresh, A. (2021). Energy efficient cluster based routing protocol using charged system harmony search algorithm in WSN. Wireless Personal Communications, 121, 1457–1470.

    Article  Google Scholar 

  16. Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48–70.

    Article  Google Scholar 

  17. Ge, Y., Nan, Y., & Chen, Y. (2020). Maximizing information transmission for energy harvesting sensor networks by an uneven clustering protocol and energy management. KSII Transactions on Internet and Information Systems (TIIS), 14(4), 1419–1436.

    Google Scholar 

  18. Sahoo, B. M., Pandey, H. M., & Amgoth, T. (2021, January). A whale optimization (WOA): meta-heuristic based energy improvement clustering in wireless sensor networks. In 2021 11th international conference on cloud computing, data science and engineering (confluence) (pp. 649–654). IEEE.

  19. Hu, J., Luo, J., Zheng, Y., & Li, K. (2018). Graphene-grid deployment in energy harvesting cooperative wireless sensor networks for green IoT. IEEE Transactions on Industrial Informatics, 15(3), 1820–1829.

    Article  Google Scholar 

  20. Sharma, D., & Bhondekar, A. P. (2019). An improved cluster head selection in routing for solar energy-harvesting multi-heterogeneous wireless sensor networks. Wireless Personal Communications, 108(4), 2213–2228.

    Article  Google Scholar 

  21. Tang, C., Tan, Q., Han, Y., An, W., Li, H., & Tang, H. (2016). An energy harvesting aware routing algorithm for hierarchical clustering wireless sensor networks. KSII Transactions on Internet and Information Systems, 10(2), 504–521.

    Google Scholar 

  22. Dong, Y., Wang, J., Shim, B., & Kim, D. I. (2016). DEARER: A distance-and-energy-aware routing with energy reservation for energy harvesting wireless sensor networks. IEEE Journal on Selected Areas in Communications, 34(12), 3798–3813.

    Article  Google Scholar 

  23. Haq, I. U., Javaid, Q., Ullah, Z., Zaheer, Z., Raza, M., Khalid, M., & Khan, S. (2020). E2-MACH: Energy efficient multi-attribute based clustering scheme for energy harvesting wireless sensor networks. International Journal of Distributed Sensor Networks, 16(10), 1550147720968047.

    Article  Google Scholar 

  24. Ren, Q., & Yao, G. (2019). An energy-efficient cluster head selection scheme for energy-harvesting wireless sensor networks. Sensors, 20(1), 187.

    Article  Google Scholar 

  25. Bozorgi, S. M., Rostami, A. S., Hosseinabadi, A. A. R., & Balas, V. E. (2017). A new clustering protocol for energy harvesting-wireless sensor networks. Computers and Electrical Engineering, 64, 233–247.

    Article  Google Scholar 

  26. Saeed, N., Celik, A., Al-Naffouri, T. Y., & Alouini, M. S. (2019). Localization of energy harvesting empowered underwater optical wireless sensor networks. IEEE Transactions on Wireless Communications, 18(5), 2652–2663.

    Article  Google Scholar 

  27. Azarhava, H., & Niya, J. M. (2020). Energy efficient resource allocation in wireless energy harvesting sensor networks. IEEE Wireless Communications Letters, 9(7), 1000–1003.

    Google Scholar 

  28. Li, M., Liu, C., & Li, Q. (2020). Energy collaboration for non-homogeneous energy harvesting in cooperative wireless sensor networks. IEEE Access, 8, 27027–27037.

    Article  Google Scholar 

  29. Gupta, S. S., & Mehta, N. B. (2018). Revisiting effectiveness of energy conserving opportunistic transmission schemes in energy harvesting wireless sensor networks. IEEE Transactions on Communications, 67(4), 2968–2980.

    Article  Google Scholar 

  30. Deng, X., Guan, P., Hei, C., Li, F., Liu, J., & Xiong, N. (2021). An intelligent resource allocation scheme in energy harvesting cognitive wireless sensor networks. IEEE Transactions on Network Science and Engineering, 8(2), 1900–1912.

    Article  MathSciNet  Google Scholar 

  31. Karami, A., & Guerrero-Zapata, M. (2015). A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks. Neurocomputing, 149, 1253–1269.

    Article  Google Scholar 

  32. Mittal, N., Singh, U., & Sohi, B. S. (2017). A novel energy efficient stable clustering approach for wireless sensor networks. Wireless Personal Communications, 95, 2947–2971.

    Article  Google Scholar 

  33. Alghamdi, T. A. (2020). Energy efficient protocol in wireless sensor network: Optimized cluster head selection model. Telecommunication Systems, 74, 331–345.

    Article  Google Scholar 

  34. Chaurasia, S., & Kumar, K. (2023). MOORP: Metaheuristic based optimized opportunistic routing protocol for wireless sensor network. Wireless Personal Communications, 132, 1241–1272.

    Article  Google Scholar 

  35. Wang, H., Li, K., & Pedrycz, W. (2020). An elite hybrid metaheuristic optimization algorithm for maximizing wireless sensor networks lifetime with a sink node. IEEE Sensors Journal, 20(10), 5634–5649.

    Article  Google Scholar 

  36. Al-Qamaji, A., & Atakan, B. (2022). Event distortion-based clustering algorithm for energy harvesting wireless sensor networks. Wireless Personal Communications, 123, 3823–3824.

    Article  Google Scholar 

  37. 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 

  38. Sahoo, B. M., Amgoth, T., & Pandey, H. M. (2021). Enhancing the network performance of wireless sensor networks on meta-heuristic approach: Grey Wolf Optimization. In Applications of artificial intelligence and machine learning: Select proceedings of ICAAAIML 2020 (pp. 469–482). Springer.

Download references

Acknowledgements

The authors would like to thank researchers, reviewers and editors.

Funding

The authors declare that they have no known competing financial interests in this paper.

Author information

Authors and Affiliations

Authors

Contributions

Biswa Mohan Sahoo concept, design, analysis, writing—original draft, writing—review and editing. Abadhan Saumya Sabyasachi concept, design, analysis, writing—original draft.

Corresponding author

Correspondence to Biswa Mohan Sahoo.

Ethics declarations

Conflict of interest

All authors have no conflict of interest to report.

Ethical Approval

Not applicable.

Consent for Publication

As per journal policy.

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

Sahoo, B.M., Sabyasachi, A.S. A Metaheuristic Algorithm Based Clustering Protocol for Energy Harvesting in IoT-Enabled WSN. Wireless Pers Commun 136, 385–410 (2024). https://doi.org/10.1007/s11277-024-11270-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11270-5

Keywords

Navigation