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An Improved Multi-hop LEACH Protocol Based on Chaotic Genetic Algorithm for Wireless Sensor Networks

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Abstract

Clustering in LEACH and its successors has been proved to be effective for not only improving energy efficiency but also extending network lifetime of wireless sensor networks. However, minimization of network energy consumption is still the most important topic in the research of hierarchical protocols based on LEACH. In this paper, an improved multi-hop LEACH protocol based on chaotic genetic algorithm (ICGA-LEACH) is proposed to obtain the optimal solution for energy efficiency and load balance at the same time. In ICGA-LEACH, cluster heads (CHs) are selected by a modified probability equation similar to LEACH, and then chaotic genetic algorithm is used to find the optimal routing paths and cluster members for the CHs according to a new constructed fitness function. Additionally, an adaptive round time is presented to further reduce energy consumption and prolong network lifetime. Simulation results in Matlab indicate that ICGA-LEACH is significantly superior to the existing relevant counterparts.

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

References

  1. Zhou, Z., Liu, J., & Mao, C. (2024). Age of information oriented data collection via energy-constrained UAVs in wireless sensor networks. IEEE Access, 12, 11897–11908.

    Article  Google Scholar 

  2. Malik, M., Joshi, A., & Sakya, G. (2023). Optimized leach protocol for energy management in wireless sensor network. Multimedia Tools Applications, 07, 1–22.

    Google Scholar 

  3. Singh, J., Deepika, J., Zaheeruddin, et al. (2022). Energy-efficient clustering and routing algorithm using hybrid fuzzy with grey wolf optimization in wireless sensor networks. Security and Communication Networks, 2022(5), 1–12.

    Google Scholar 

  4. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences. Piscataway, NJ. 3eneti005-3014. IEEE

  5. Gupta, V., & Pandey, R. (2016). An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. International Journal of Computer Network and Information Security, 6(19), 29–37.

    Article  Google Scholar 

  6. Pal, R., Saraswat, M., Kumar, S., et al. (2023). Energy efficient multi-criterion binary grey wolf optimizer based clustering for heterogeneous wireless sensor networks. Soft Computing, 10, 1–15.

    Google Scholar 

  7. Giri, A., Dutta, S., & Neogy, S. (2022). An optimized fuzzy clustering algorithm for wireless sensor networks. Wireless Personal Communications, 126(3), 2731–2751.

    Article  Google Scholar 

  8. Fadwa, A. M., Nagham, M., Hassan, H. S., et al. (2022). Sectored LEACH (S-LEACH): an enhanced LEACH for wireless sensor network. IET Wireless Sensor Systems, 12(2), 56–66.

    Article  Google Scholar 

  9. Lee, J. Y., Jung, K. D., Moon, S. J., & Jeong, H. Y. (2017). Improvement on LEACH protocol of a wide-area wireless sensor network. Multimedia Tools Applications, 2017(76), 19843–19860.

    Article  Google Scholar 

  10. Huang, W. W., Ling, Y., & Zhou, W. L. (2018). An improved LEACH routing algorithm for wireless sensor network. International Journal of Wireless Information Networks, 2018(25), 323–331.

    Article  Google Scholar 

  11. Ding, X. X., Ling, M., Wang, Z. J., & Song, F. L. (2017). DK-LEACH: An optimized cluster structure routing method based on LEACH in wireless sensor networks. Wireless Personal Communications, 2017(96), 6369–6379.

    Article  Google Scholar 

  12. Arumugam, G. S., & Ponnuchamy, T. (2015). EE-LEACH: development of energy-efficient LEACH Protocol for data gathering in WSN. Eurasip Journal on Wireless Communications & Networking, 2015(1), 1–9.

    Article  Google Scholar 

  13. Hong, J., Kook, J., Lee, S., Kwon, D., & Yi, S. (2009). T-LEACH: the method of threshold-based cluster head replacement for wireless sensor networks. Information Systems Frontiers, 11(5), 513–521.

    Article  Google Scholar 

  14. Ikram, D., & Abdennaceur, B. (2022). IBRE-LEACH: improved the performance of the BRE-LEACH for wireless sensor networks. Wireless Personal Communications, 126(4), 3495–3513.

    Article  Google Scholar 

  15. Sodairi, S. A., & Ouni, R. (2018). Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks. Sustainable Computing: Informatics and Systems, 2018(20), 1–13.

    Google Scholar 

  16. Maratha, P., & Gupta, K. (2022). HFLBSC: heuristic and fuzzy based load balanced, scalable clustering algorithm for wireless sensor network. Wireless Personal Communications, 125(1), 281–304.

    Article  Google Scholar 

  17. Daanoune, I., Abdennaceur, B., & Ballouk, A. (2021). A comprehensive survey on LEACH-based clustering routing protocols in wireless sensor networks. Ad Hoc Networks, 114, 1–21.

    Article  Google Scholar 

  18. Mohammed, A. S., Mohammed, A., Tat-chee, W., et al. (2019). Energy efficient multi-hop in wireless sensor networks using an enhanced genetic algorithm. Information Science, 2019(500), 259–273.

    Google Scholar 

  19. Zhao, L., Qu, S., & Yi, Y. (2018). A modified cluster-head selection algorithm in wireless sensor networks based on LEACH. EURASIP Journal on Wireless Communications and Networking, 2018(1), 1–8.

    Article  Google Scholar 

  20. Mohammed, A. S., Mohammed, A., Tat, C. W., et al. (2018). Variants of the low-energy adaptive clustering hierarchy protocol: survey, issues and challenges. Electronics, 7(136), 1–28.

    Google Scholar 

  21. Ge, R., Zhang, H. Z., & Gong, S. L. (2010). Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Journal of Information & Computational Science, 7(3), 767–775.

    Google Scholar 

  22. Tarunpreet, B., Simmi, K., Shivani, G., et al. (2016). A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers and Electrical Engineering, 2016(56), 441–455.

    Google Scholar 

  23. Cai, X. J., Sun, Y. Q., Cui, Z. H., et al. (2019). Optimal LEACH protocol with improved bat algorithm in wireless sensor networks. KSII Transactions on Internet and Information System, 13(5), 2469–2490.

    Google Scholar 

  24. Ying, Z., Li, P. S., & Mao, L. (2018). Research on improved low-energy adaptive clustering hierarchy protocol in wireless sensor networks. Journal of Shanghai Jiao Tong University, 23(5), 613–619.

    Google Scholar 

  25. Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010). MR-LEACH: multi-hop routing with low energy adaptive clustering hierarchy. In: 2010 fourth international conference on sensor technologies and applications (pp. 262-268). IEEE.

  26. Arjunan, S., & Pothula, S. (2021). A survey on unequal clustering protocols in Wireless Sensor Networks. Journal of King Saud University Computer and Information Sciences, 2021(33), 304–317.

    Google Scholar 

  27. Agrawal, D., & Pandey, S. (2018). FUCA: Fuzzy-based unequal clustering algorithm to prolong the lifetime of wireless sensor networks. International Journal of Communication Systems, 31(2), e3448.

    Article  Google Scholar 

  28. Gunjan, S. A. K., & Verma, K. (2023). GA-UCR: Genetic algorithm based unequal clustering and routing protocol for wireless sensor networks. Wireless Personal Communications, 128(1), 537–558.

    Article  Google Scholar 

  29. Cao, Y., & Muqing, Wu. (2018). A novel RPL algorithm based on chaotic genetic algorithm. Sensors, 18(3647), 1–20.

    Google Scholar 

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Acknowledgements

We are grateful to the anonymous reviewers who have contributed to the enhancement of the paper’s completeness with their valuable suggestions.

Funding

This work are supported by the science and technology development projects of Jilin province [Grant numbers 20210201051GX and 20210203161SF], and the education department project of Jilin province [Grant number JJKH20220686KJ].

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Authors and Affiliations

  1. College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, China

    Wang Chuhang

  2. Jilin Province Smart Health Joint Innovation Laboratory for the New Generation of AI, University of Technology and Engineering, Changchun, 130012, China

    Hu Huangshui

  3. College of Computer Science and Engineering, Changchun University of Technology, Changchun, 130012, China

    Wang Tingting

Authors
  1. Wang Chuhang
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  2. Hu Huangshui
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  3. Wang Tingting
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Wang Chuhang, Hu Huangshui and Wang Tingting. The first draft of the manuscript was written by Wang Chuhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wang Chuhang.

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Chuhang, W., Huangshui, H. & Tingting, W. An Improved Multi-hop LEACH Protocol Based on Chaotic Genetic Algorithm for Wireless Sensor Networks. Wireless Pers Commun 134, 1843–1862 (2024). https://doi.org/10.1007/s11277-024-10988-6

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  • DOI: https://doi.org/10.1007/s11277-024-10988-6

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