Log in

Cluster-Based Hybrid Routing Technique for Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

While deploying wireless sensor networks (WSNs), the cluster heads need huge amount of energy according to the unbalanced routing of the Sensor nodes to the base station as the result, which has produced minimized network lifetime and unbalanced energy utilization. The proposed cluster-based hybrid routing technique (CHRT) contains the cluster head selection with effective energy utilization procedure which extends the network lifetime and enhanced packet routing technique is used to reduce the energy of the sensor node with the Euclidean distance metric, base station location identification and residual energy. The fitness function is used for selecting the cluster heads for enhancing the selection of the cluster head in efficient way. The modified fitness function has been introduced for relaying the remaining cluster heads through enhanced routing functionality. The simulation results of the proposed technique suggested that it enhances the network lifetime, improves the residual energy and coverage area as compared to the relevant methodologies.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /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 includes VAT (Spain)

Instant access to the full article PDF.

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

Data Availability

Not applicable.

Code Availability

Not applicable.

References

  1. A, Raychaudhuri, D, De, (2020). “Bio-inspired algorithm for multi-objective optimization in wireless sensor network,” In in Nature Inspired Computing for Wireless Sensor Networks, (pp. 279–301), Springer.

  2. Sun, Z., Wei, M., Zhang, Z., & Qu, G. (2019). Secure routing protocol based on multi-objective ant-colony-optimization for wireless sensor networks. Applied Soft Computing, 77, 366–375.

    Article  Google Scholar 

  3. J, Lou, (2019). “Location-based multi-objective optimization routing algorithm for WSN,” in In Recent developments in intelligent computing, communication and devices, (pp. 523–529), Springer

  4. Liu, J., Shen, H., Yu, L., et al. (2017). Characterizing data deliverability of greedy routing in wireless sensor networks. IEEE Transactions on Mobile Computing, 17(3), 543–559.

    Article  Google Scholar 

  5. Singh, S., & Kumar, P. (2020). MH-CACA: Multi-objective harmony search-based coverage aware clustering algorithm in WSNs. Enterprise Information Systems, 14(9–10), 1325–1353.

    Article  Google Scholar 

  6. Wang, J., Ju, C., Gao, Y., Sangaiah, A. K., Kim, G., et al. (2018). A PSO based energy efficient coverage control algorithm for wireless sensor networks. Computers, Materials & Continua, 56(3), 433–446.

    Google Scholar 

  7. Wang, J., Gao, Y., Zhou, C., Simon Sherratt, R., & Wang, L. (2020). Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs. Computers, Materials & Continua, 62(2), 695–711.

    Article  Google Scholar 

  8. Guruprakash, B., Balasubramanian, C., & Sukumar, R. (2020). An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN. Peer-to-Peer Networking and Applications, 13(1), 304–319.

    Article  Google Scholar 

  9. J, Kumari, (2015). “A comprehensive survey of routing protocols in wireless sensor networks,” In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India

  10. M, Malakar, (2020). “TLBO based cluster-head selection for multi-objective optimization in wireless sensor networks,” In in Nature Inspired Computing for Wireless Sensor Networks, pp. 303–319, Springer

  11. Heinzelman WR, Chandrakasan A, Balakrishnan H, (2000). “Energy-efficient communication protocol for wireless microsensor networks,” Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, (vol.2, pp. 10). Maui, HI, USA.

  12. 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.

    Article  Google Scholar 

  13. Verma, A., Kumar, S., Gautam, P., Rashid, T., & Kumar, A. (2020). Fuzzy logic based effective clustering of homogeneous wireless sensor networks for mobile sink. IEEE Sensors Journal, 20(10), 5615–5623.

    Article  Google Scholar 

  14. I. Daanoune, A. Baghdad and A. Balllouk, (2019). “BRE-LEACH: A new approach to extend the lifetime of wireless sensor network,” 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), (pp. 1–6). Marrakech, Morocco.

  15. Nayak, P., & Devulapalli, A. (2016). A Fuzzy logic-based clustering algorithm for wsn to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.

    Article  Google Scholar 

  16. Liao Q, Zhu, (2013). “An energy-balanced clustering algorithm based on LEACH protocol”, In ICSEM-13–2nd International Conference on Systems Engineering and Modeling, (vol. 5, no. 3, pp. 90–95).

  17. Rajpoot P, Dwivedi P, Dubey K, (2019). “Power balanced efficient clustering algorithm for WSN,” 2019 International Conference on Communication and Electronics Systems (ICCES), (pp. 585–589).Coimbatore, India.

  18. Azad, P., & Sharma, V. (2013). Cluster head selection in wireless sensor networks under fuzzy environment. International Scholarly Research Notices, 2013(909086), 8.

    Google Scholar 

  19. Wei, Q., Bai, K., Zhou, L., Hu, Z., **, Y., & Li, J. (2021). A cluster-based energy optimization algorithm in wireless sensor networks with mobile sink. Sensors, 21, 2523.

    Article  Google Scholar 

  20. Panchal, A. (2021). Rajat Kumar Singh, “EOCGS: Energy efficient optimum number of cluster head and grid head selection in wireless sensor networks.” Telecommunication Systems, 78, 1–13.

    Article  Google Scholar 

  21. Muhammed Tay, Arafat Senturk, (2021). “A new energy-aware cluster head selection algorithm for wireless sensor networks”, Wireless Personal Communications.

  22. Saleh, S. S., Mabrouk, T. F., & Tarabishi, R. A. (2021). An improved energy-efficient head election protocol for clustering techniques of wireless sensor network (June 2020). Egyptian Informatics Journal, 22(4), 439–445.

    Article  Google Scholar 

  23. Kathiroli, P., & Selvadurai, K. (2021). Energy efficient cluster head selection using improved sparrow search algorithm in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 34, 8564–8575.

    Article  Google Scholar 

  24. Doryanizadeh, V., Keshavarzi, A., Derikvand, T., & Bohlouli, M. (2021). Energy efficient cluster head selection in internet of things using minimum spanning tree (EEMST). Applied Artificial Intelligence, 35, 1777–1802.

    Article  Google Scholar 

  25. Yagoub, M. F. S., Khalifa, O. O., Abdelmaboud, A., Korotaev, V., Kozlov, S. A., & Rodrigues, J. P. C. J. (2021). “Lightweight and efficient dynamic cluster head election routing protocol for wireless sensor networks.” Sensors, 21(15), 5206.

    Article  Google Scholar 

  26. Nabavi, S. R., Ostovari Moghadam, V., Yahyaei Feriz Hendi, M., & Ghasemi, A. (2021). Optimal selection of the cluster head in wireless sensor networks by combining the multiobjective genetic algorithm and the gravitational search algorithm. Journal of Sensors, 2021(2292580), 16.

    Google Scholar 

  27. Aydin, M. A., Karabekir, B., & Zaim, A. H. (2021). Energy efficient clustering-based mobile routing algorithm on WSNs. IEEE Access, 9, 89593–89601.

    Article  Google Scholar 

  28. Sharma, V., & Grover, A. (2016). A modified ant colony optimization algorithm (mACO) for energy efficient wireless sensor networks. Optik, 127(4), 2169–2172.

    Article  Google Scholar 

  29. Grover, A., Kumar, R. M., Angurala, M., Singh, M., Sheetal, A., & Maheswar, R. (2022). Rate aware congestion control mechanism for wireless sensor networks. Alexandria Engineering Journal, 61(6), 4765–4777.

    Article  Google Scholar 

  30. Grover, A., Singh, H., Chhabra, N., et al. (2022). Finding an appropriate radio propagation model for rate aware congestion control mechanism in wireless sensor networks. Wireless Networks, 28, 3045–3057.

    Article  Google Scholar 

Download references

Acknowledgements

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R192), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

This work has not supported by any funding agency/institution.

Author information

Authors and Affiliations

Authors

Contributions

Y. Harold Robinson: Writing—original draft, Writing—review & editing, Conceptualization, Data curation. B. Valarmathi: Writing—original draft Conceptualization, Data curation. P. Srinivasan: Validation, Formal analysis, Supervision. Hanen Karamti: Conceptualization, Data curation.

Corresponding author

Correspondence to Y. Harold Robinson.

Ethics declarations

Conflict of interest

The authors declare that they do not have any conflict of interest. This research does not involve any human or animal participation. All authors have checked and agreed the submission.

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

Robinson, Y.H., Valarmathi, B., Srinivasan, P. et al. Cluster-Based Hybrid Routing Technique for Wireless Sensor Networks. Wireless Pers Commun (2024). https://doi.org/10.1007/s11277-024-11406-7

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11277-024-11406-7

Keywords

Navigation