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Nature-inspired routing and optical communication for next-generation IoT systems

  • Rajni Rani ORCID logo EMAIL logo and Sharad Sharma ORCID logo
Published/Copyright: February 6, 2025
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Abstract

In this paper, we explore the interconnection of natural processes and current technologies to satisfy the emerging requirements of next-generation Internet of Things (IoT) ecosystems. Motivated by biological networks, we propose a novel routing and optical communication approach that leverages nature’s ability for self-organization, flexibility and resilience. This paper presents a model for bio-inspired routing protocols to be used in Internet of Things networks in order to improve scalability, reduce latency and maximize data transmission efficiency. Moreover, we investigate the potential of optical communication technologies, which are fast, low-latency and energy-efficient, to support the immense data transmission requirements of Internet of Things systems. The research finds key barriers and opportunities for integrating nature-based approaches.


Corresponding author: Rajni Rani, Electronics and Communication Department, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, India, E-mail:

Acknowledgments

No other person are included here I Rajni Rani Research Scholar is only corresponding author.

  1. Research ethics: Yes I will follow all the research ethics and guidelines.

  2. Informed consent: Yes.

  3. Author contributions: Author A: Conceptualization, Methodology, Investigation, Writing – Original Draft. Author B: Supervision, Validation, Writing – Review & Editing (For double-anonymized journals) Dr. Sharad Sharma is my Supervisor.

  4. Use of Large Language Models, AI and Machine Learning Tools: No large language models, AI tools, or machine learning algorithms were used in the creation or analysis of the research presented in this article.

  5. Conflict of interest: No.

  6. Research funding: The research was not supported by any external funding.

  7. Data availability: Not applicable.

References

1. Yang, XS. Nature-inspired metaheuristic algorithms. Beckington, UK: Luniver Press; 2008.Search in Google Scholar

2. Fister, I, Yang, XS, Brest, J. A comprehensive review of firefly algorithms. Swarm Evol Comput 2013;13:34–46. https://doi.org/10.1016/j.swevo.2013.06.001.Search in Google Scholar

3. Gandomi, AH, Yang, XS. Firefly algorithm: a comparative review. J Future Generat Comput Syst 2014;29:324–34. https://doi.org/10.1016/j.future.2012.09.017.Search in Google Scholar

4. Yadav, V, Gupta, A. Particle swarm optimization for energy-efficient IoT routing: a comprehensive survey. IEEE Trans Netw Serv Manag 2020;17:2768–82. https://doi.org/10.1109/TNSM.2020.3027681.Search in Google Scholar

5. Kumar, S, Ghosh, S. A comprehensive review of ant colony optimization for IoT routing protocols. Soft Comput 2021;25:3565–81. https://doi.org/10.1007/s00500-020-04758-5.Search in Google Scholar

6. Tariq, M, Khan, F. Hybrid nature-inspired algorithms for IoT Gao, J., Wang, Q. Nature-inspired algorithms for IoT network optimization: a survey of recent advances. Comput Environ Urban Syst 2021;89:101665. https://doi.org/10.1016/j.compenvurbsys.2021.101665.Search in Google Scholar

7. Ishikawa, M, Kim, S. Comparative analysis of nature-inspired routing protocols for IoT: a comprehensive review. J Netw Comput Appl 2023;207:103451. https://doi.org/10.1016/j.jnca.2023.103451.Search in Google Scholar

8. Zang, H, Zhang, S, Hapeshi, K. A review of nature-inspired algorithms. J Bionic Eng 2010;7:S232–7. https://doi.org/10.1016/s1672-6529(09)60240-7.Search in Google Scholar

9. Zhang, L, Wang, M, Zhang, S. Nature-inspired computing for IoT data management: a comprehensive review. IEEE Access 2023;20:1234–56.Search in Google Scholar

10. Ghanbari, A, Jafari, A, Rahimi, M, Chen, X, Kumar, R, Ahmed, M, et al.. Hybrid metaheuristic approaches in IoT and smart grid: applications and future directions. IEEE Access 2022;10:47812–36. https://doi.org/10.1109/ACCESS.2022.3173815.Search in Google Scholar

11. Hasim, MM, Zhou, Y, Chen, L, Wang, Q, Kaur, S, Khan, F, et al.. Nature-inspired optimization algorithms in IoT: a review. Comput Environ Urban Syst 2022;89:101665. https://doi.org/10.1016/j.compenvurbsys.2021.101665.Search in Google Scholar

12. Xue, W, Sun, Y, Gao, P, Lee, TY, Hong, Z, Yang, F, et al.. A review of nature-inspired algorithms for big data in IoT: trends and challenges. J Netw Comput Appl 2022;207:103506. https://doi.org/10.1016/j.jnca.2023.103506.Search in Google Scholar

13. Yang, XS, Zhang, Q, Li, P, Zhao, W, Zhou, S, Lin, M, et al.. Nature-inspired algorithms for smart cities: a comprehensive survey. IEEE Access 2024;32:1013–2024.Search in Google Scholar

14. Zhang, Q, Wang, H, Li, Y, Chen, J, Sun, P, Zhang, T. A firefly algorithm based approach for the multi-objective optimization in IoT networks. Soft Comput 2023;27:5107–18. https://doi.org/10.1007/s00500-022-06036-1.Search in Google Scholar

15. Prasad, NR, Singh, K, Ahmed, Z, Kumar, S, Yadav, P, Roy, R. An improved firefly algorithm for IoT data routing in a smart grid environment. IEEE Trans Smart Grid 2023;14:4275–84. https://doi.org/10.1109/TSG.2023.3278364.Search in Google Scholar

16. Khraisat, A, Al-Maadeed, S, Kumar, N, Chen, W, Liu, Y, Lee, C, et al.. Nature-inspired computing for IoT devices: a systematic review. J Netw Comput Appl 2023;220:103409. https://doi.org/10.1016/j.jnca.2023.103409.Search in Google Scholar

17. Tiwari, SK, Verma, A, Patel, R, Roy, S, Mukherjee, S, Jha, A, et al.. A comparative review of nature-inspired algorithms for IoT data processing. Comput Environ Urban Syst 2024;90:101633. https://doi.org/10.1016/j.compenvurbsys.2023.101633.Search in Google Scholar

18. Ahmed, N, Gupta, R, Kumar, V, Lee, D, Yoon, H, Park, S, et al.. Hybrid approaches to routing optimization in IoT networks: a survey. J Syst Architect 2022;128:102472. https://doi.org/10.1016/j.sysarc.2021.102472.Search in Google Scholar

19. Yang, X, Li, J. Ant colony optimization for routing in internet of things: an updated review. IEEE Internet Things J 2022;9:4501–14. https://doi.org/10.1109/JIOT.2021.3086145.Search in Google Scholar

20. Wang, L, Zhang, Y. Ant colony optimization-based energy-efficient routing in IOT networks. Comput Commun 2021;168:59–73. https://doi.org/10.1016/j.comcom.2020.10.012.Search in Google Scholar

21. Sharma, S, Singh, R. Nature-inspired algorithms for IOT routing: challenges and opportunities. IEEE Trans Emerg Top Comput 2023;11:120–34. https://doi.org/10.1109/TETC.2023.3245671.Search in Google Scholar

22. Maher, B, Albrecht, AA, Loomes, M, Yang, XS, Steinh¨ofel, K. A firefly-inspired method for protein structure prediction in lattice models. Biomolecules 2014;4:56–75. https://doi.org/10.3390/biom4010056.Search in Google Scholar PubMed PubMed Central

23. Yu, SH, Zhu, SL, Ma, Y, Mao, DM. A variable step size firefly algorithm for numerical optimization. Appl Math Comput 2015;263:214–20. https://doi.org/10.1016/j.amc.2015.04.065.Search in Google Scholar

24. Agarwal, V, Surekha, B. Firefly inspired feature selection for face recognition. In: 2015 Eighth international conference on contemporary computing (IC3). IEEE; 2015.10.1109/IC3.2015.7346689Search in Google Scholar

Received: 2024-12-01
Accepted: 2024-12-31
Published Online: 2025-02-06

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 12.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/joc-2024-0295/html
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