Dynamic Routing in Mobile Ad-Hoc Networks using AI-Powered Optical Waveguides
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Vikas Sharma
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Pritibha Sukhroop
Abstract
Mobile Ad-Hoc Networks (MANETs) are decentralized, self-organizing networks with mobile nodes that communicate wirelessly without relying on a fixed infrastructure. Despite their potential, MANETs face numerous challenges, including dynamic network topology, limited bandwidth, and unreliable communication links. The integration of optical waveguides in MANETs can address some of these challenges by enabling high-speed data transfer and improving network capacity. However, the dynamic nature of MANETs requires efficient and adaptive routing protocols to ensure stable and reliable communication. Artificial Intelligence (AI) techniques, particularly machine learning and deep learning, offer promising solutions to optimize path discovery, traffic management, and decision-making processes in these networks. This paper explores the application of AI-driven optical waveguides for dynamic routing in MANETs, highlighting their potential to optimize routing performance, enhance network reliability, and reduce latency in real-time operations. By leveraging AI techniques for intelligent routing decisions, optical waveguides can significantly improve the efficiency and scalability of MANETs, paving the way for more advanced mobile communication systems.
Acknowledgments
Thanks to all my co author for the support.
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Research ethics: Not applicable.
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Informed consent: We all are fully responsible for this paper.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
1. Zhang, H, Zhang, Y. AI-based routing algorithms for MANETs: a survey and future directions. Int J Comput Sci Inf Secur 2021;19:12–22.Search in Google Scholar
2. Shah, A, Rahman, M. Artificial intelligence techniques in MANETs: applications and challenges. Comput Netw 2020;174:107–20.Search in Google Scholar
3. Alemu, G, Jafarian, A. Reinforcement learning for efficient routing in mobile Ad hoc networks. Int J Mobile Comput Multimed Commun 2021;13:28–42.Search in Google Scholar
4. Liu, L, Li, B. Free-space optical communications for MANETs: recent advances and future trends. J Opt Commun Netw 2020;12:350–8.Search in Google Scholar
5. Khan, S, Raza, S. AI-based routing for smart mobile networks: integration of optical waveguides. Mobile Network Appl 2022;27:1–11.Search in Google Scholar
6. Hassan, M, Akram, M. Artificial intelligence for traffic management in hybrid wireless networks. J Wirel Commun Netw 2021;2021:20–30.Search in Google Scholar
7. Wang, T, Zhang, X. Machine learning algorithms for dynamic routing in MANETs. IEEE Trans Mobile Comput 2019;18:760–72.Search in Google Scholar
8. Jang, S, Park, Y. Intelligent routing protocols in mobile Ad-hoc networks using reinforcement learning. Sensors 2021;21:3813–29.Search in Google Scholar
9. Li, F, Yu, X. Optical networks in mobile Ad-hoc environments: a survey on Waveguide-based routing. Opt Switch Netw 2020;36:48–59.Search in Google Scholar
10. Ahmed, Z, Hossain, M. Artificial intelligence in MANETs: challenges and future directions. IEEE Access 2021;9:10567–76.Search in Google Scholar
11. Bai, L, Hu, L. Deep reinforcement learning for routing optimization in optical MANETs. Trans Network Serv Manag 2019;16:1462–74.Search in Google Scholar
12. Zhu, Z, Wang, S. AI-based path discovery in optical-enabled MANETs. J Opt Commun Netw 2022;14:168–79.Search in Google Scholar
13. Ghosh, P, Tripathi, A. Optimization of routing algorithms using machine learning in MANETs. Computers 2020;9:130–46.Search in Google Scholar
14. Yang, K, Zhao, F. A survey on AI-driven routing protocols for mobile Ad-hoc networks. Comput Commun 2020;151:75–88.Search in Google Scholar
15. Chen, C, Liu, J. Hybrid AI models for dynamic routing in optical-based MANETs. IEEE Trans Wireless Commun 2021;20:345–56.Search in Google Scholar
16. Zhang, Z, Liu, Z. AI and machine learning techniques for efficient mobile Ad-hoc networks routing. IEEE Wireless Commun 2020;27:54–62.Search in Google Scholar
17. Zhao, Z, Zhang, S. Optical communication networks in MANETs: challenges and opportunities. Photonic Netw Commun 2021;41:506–15.Search in Google Scholar
18. Nguyen, T, Tran, D. Reinforcement learning for load balancing in mobile Ad-hoc networks. Comput Netw 2019;160:80–91.Search in Google Scholar
19. Iqbal, S, Alharthi, M. An AI-based approach for optimized routing in wireless networks. Int J Wireless Mobile Comput 2020;20:406–17.Search in Google Scholar
20. Sangwan, P, Gupta, N. Artificial intelligence for network traffic management in optical-based mobile Ad-hoc networks. J Netw Syst Manag 2021;29:125–37.Search in Google Scholar
21. Jiang, C, Cao, B. AI for quality-of-service routing in optical MANETs. Int J Commun Syst 2021;34:419–33.Search in Google Scholar
22. Liu, H, Wang, Q. Optimization of routing protocols in optical communication-based MANETs using AI. IEEE J Sel Area Commun 2021;39:601–13.Search in Google Scholar
23. Gurugubelli, V, Saxena, A. Applications of machine learning for routing in optical Ad-hoc networks. Int J Comput Sci Eng 2021;14:552–63.Search in Google Scholar
24. Zhu, Y, Du, Y. AI-enhanced dynamic routing in optical Waveguide-based MANETs. Opt Fiber Technol 2020;54:1–9.Search in Google Scholar
25. Xu, B, Xie, M. AI-based smart routing for hybrid optical and RF MANETs. Mobile Network Appl 2022;27:179–90.Search in Google Scholar
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