A comparative study of routing protocols, AI, and passive optical networks in the evolution of mobile ad hoc networks (MANETS)
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Vikas Sharma
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Pritibha Sukhroop
Abstract
Mobile Ad Hoc Networks (MANETs) have gained significant attention in wireless communication due to their decentralized nature, flexibility, and adaptability. However, MANETs face challenges such as dynamic topologies, limited resources, and unpredictable mobility patterns, necessitating intelligent routing protocols for optimal performance. This research presents a comparative study of traditional MANET routing protocols and recent advancements incorporating Artificial Intelligence (AI). We analyze the improvements AI brings to routing efficiency by comparing AI-driven techniques with conventional methods. Additionally, the paper investigates the integration of Passive Optical Networks (PONs) with MANETs, focusing on how optical technologies can enhance network performance by providing high-capacity, low-latency backhaul links. These links could complement AI-based routing decisions, improving overall network stability and data transmission. The study highlights both the opportunities and challenges of merging AI with traditional routing protocols and PONs, offering insights into future research directions aimed at optimizing MANET performance. The synergy between AI and PONs may offer a promising solution to address the dynamic and resource-constrained nature of MANETs in real-world applications.
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. Perkins, CE, Royer, EM. Ad-Hoc On-Demand Distance Vector Routing. New Orleans, LA, USA: Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications; 1999.10.1109/MCSA.1999.749281Search in Google Scholar
2. Johnson, DB, Maltz, DA. Dynamic source routing in ad hoc wireless networks. Mobile Computing 1996;353:153–81. https://doi.org/10.1007/978-0-585-29603-6_5.Search in Google Scholar
3. Clausen, T, Jacquet, P. Optimized Link State Routing Protocol (OLSR). RFC 3626. Lahore, Pakistan: IETF; 2003.10.17487/rfc3626Search in Google Scholar
4. Perkins, CE, Bhagwat, P. Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. Comput Commun Rev 1994;24:234–44. https://doi.org/10.1145/190809.190336.Search in Google Scholar
5. Haas, ZJ, Pearlman, MR, Samar, P. The Zone Routing Protocol (ZRP) for Ad Hoc Networks. Lahore, Pakistan: IETF Draft; 2002.Search in Google Scholar
6. Chakeres, ID, Perkins, CE. Dynamic MANET on-demand (DYMO) routing. In: RFC. Lahore, Pakistan: IETF; 2008, 5435.Search in Google Scholar
7. Barolli, L, Koyama, A, Shiratori, N, Yamada, S. Performance evaluation of OLSR and AODV protocols using the network simulator ns-2. Int J Commun Syst 2003;16:295–312.Search in Google Scholar
8. Manickam, P, Girija, M. A performance comparison of routing protocols in mobile ad hoc networks. Int J Wireless Mobile Network 2013;5:99–114.Search in Google Scholar
9. Chakrabarti, S, Mishra, A. QoS issues in ad hoc wireless networks. IEEE Commun Mag 2001;39:142–8. https://doi.org/10.1109/35.900643.Search in Google Scholar
10. Zafar, R, Ali, S, Wazir, M. Routing protocols for mobile ad-hoc networks based on artificial intelligence: a survey. IEEE Access 2018;6:33664–84.Search in Google Scholar
11. Barakovic, S, Barakovic, J. Comparative performance evaluation of mobile ad hoc routing protocols. J Info Technol Appl 2010;1:1–10.Search in Google Scholar
12. Mohammed, MA. MANET routing protocols evaluation: a survey. J Comput Networks Wireless Commun 2020;5:45–60.Search in Google Scholar
13. Srivastava, S, Goel, S. Energy-efficient routing in MANET using artificial intelligence techniques: a survey. J Inf Optim Sci 2017;38:571–83.Search in Google Scholar
14. Tuteja, A, Gujral, R. Comparative performance analysis of DSDV, AODV, and DSR routing protocols in MANET using NS2. In: International Conference on Advances in Computing, Control, and Telecommunication Technologies; 2009.10.1109/ACE.2010.16Search in Google Scholar
15. Yang, Z, Mohammed, M, Guo, YJ, Zhu, H. A review of machine learning algorithms applied to routing protocols in MANETs. IEEE Wireless Commun Letters 2017;6:730–3. https://doi.org/10.1109/lwc.2017.2736979.Search in Google Scholar
16. Chen, T, Zhang, J, Wu, Y. Swarm intelligence applied to MANET routing: a comprehensive survey. Int J Ad Hoc Ubiquitous Comput 2009;4:92–101.Search in Google Scholar
17. Al-Karaki, JN, Kamal, AE. Routing techniques in wireless sensor networks: a survey. IEEE Wireless Commun 2004;11:6–28. https://doi.org/10.1109/mwc.2004.1368893.Search in Google Scholar
18. Li, L, Kunz, T. Reactive routing for mobile ad hoc networks using reinforcement learning. In: Proceedings of the 7th International Symposium on Parallel Architectures, Algorithms, and Networks; 2004.Search in Google Scholar
19. Sabri, S, Habibi, D. A new routing protocol for MANET based on reinforcement learning and mobility prediction. Ad Hoc Netw 2018;76:85–99.Search in Google Scholar
20. Wu, Q, Zhang, L, Zheng, Y. Fuzzy logic-based MANET routing algorithm. Int J Commun Syst 2010;23:641–56.Search in Google Scholar
21. Dorigo, M, Stützle, T. Ant Colony Optimization. Cambridge, Massachusetts: MIT Press; 2004.10.7551/mitpress/1290.001.0001Search in Google Scholar
22. Boukerche, A, Yen, K. Ant-based algorithms for routing in mobile ad hoc networks. In: Proceedings of the IEEE International Symposium on Performance Evaluation of Computer and Telecommunication Systems; 2006.Search in Google Scholar
23. Singh, S, Sharma, M. Ant colony optimization for MANET routing. Int J Adv Res Comput Sci Software Eng 2012;2:314–20.Search in Google Scholar
24. Gupta, N, Sharma, N. Artificial intelligence-based optimization techniques in wireless ad hoc networks: a review. J Commun Network 2020;22:392–406.Search in Google Scholar
25. Geetha, J, Lingaraj, K. Machine learning-based energy-efficient routing in mobile ad hoc networks. J Netw Comput Appl 2015;58:112–20.Search in Google Scholar
26. Xu, Z, Tian, Y. Swarm intelligence in MANET: a review. J Swarm Int Res 2013;4:10–25.Search in Google Scholar
27. Mohammadi, M, Sadeghi, A. A new reinforcement learning-based routing protocol for MANETs. Int J Commun Syst 2020;33:e4427.Search in Google Scholar
28. Roy, S, Das, R. AI-based routing protocols for ad hoc networks: a comparative study. Int J Comput Network Commun 2018;10:27–45.Search in Google Scholar
29. Ramana, V. AI-based hybrid routing protocols for MANETs: a review. Int J Renew Energy Technol 2016;5:74–81.Search in Google Scholar
30. Mitra, S, Das, S. Machine learning in MANETs: a survey and performance analysis. J Netw Comput Appl 2017;88:34–47.Search in Google Scholar
31. Li, H, Ma, Y. Adaptive routing using deep reinforcement learning in MANETs. IEEE Access 2019;7:93065–75.10.1109/ACCESS.2019.2903150Search in Google Scholar
32. Kaur, S, Verma, P. A comparative study of reactive routing protocols in MANET with AI-based enhancements. Int J Wireless Commun 2020;12:12–21.Search in Google Scholar
33. Alshahrani, A, Kamal, A. Deep learning applications in MANET routing: a survey. IEEE Trans Mobile Comput 2018;17:2924–40.Search in Google Scholar
34. Zhang, Y, Liu, L. Efficient routing in MANET using Q-learning and fuzzy logic. J Wireless Mobile Networks 2021;15:213–22.Search in Google Scholar
35. Khan, A, Abbas, G. Fuzzy logic applications in MANET routing: a survey. Wirel Pers Commun 2016;89:505–32.Search in Google Scholar
36. Ren, X, Zhang, L. AI-based routing in dynamic MANETs: a comparative review. In: Proceedings of the International Conference on Wireless Networks and Systems; 2019.Search in Google Scholar
37. Sharma, N, Singh, R. Energy-aware routing protocols in MANET: an AI perspective. J Networks Appl 2021;29:67–83.Search in Google Scholar
38. Nader, B, Samir, H. Swarm intelligence-based approaches for routing in MANET: a comprehensive survey. Swarm Evol Comput 2020;58:100711. https://doi.org/10.1016/j.swevo.2020.100711.Search in Google Scholar
39. Panigrahi, P, Bhuyan, M. Bio-inspired routing protocols for MANET: a review. Int J Wireless Mobile Comput 2015;8:156–70.Search in Google Scholar
40. Pandey, S, Rai, M. Survey of routing protocols in MANET based on AI techniques. Int J Comput Appl 2012;59:8–15.Search in Google Scholar
41. Kim, D, Lee, J. Application of reinforcement learning in mobile ad hoc networks: a review. J Mobile Comput 2014;12:46–53.Search in Google Scholar
42. Singh, G, Kumar, A. A comprehensive survey of AI-enhanced routing protocols in mobile ad hoc networks. J Comput Networks Wireless Commun 2019;5:45–56.Search in Google Scholar
43. Gupta, A, Srivastava, M. A study on AI-enhanced MANET routing protocols: challenges and future directions. IEEE Access 2020;8:137784–800.Search in Google Scholar
44. Zhu, A. Introduction to Passive Optical Network (PON). 2016. Available from: http://www.fiber-optic-solutions.com/intro-optical-network-pon.html.Search in Google Scholar
45. Gasman, L. Optical Networking in 5G Backhaul: data rates versus costs; 2017. Available from: http://www.lightwaveonline.com/articles/2017/10/optical-networkingin-5g-backhaul-data-rates-versus-costs.html.Search in Google Scholar
46. FiberHome, B. The Driving Force of Network Evolution AN JUNFENF. Market Department: NBU; 2017. Available from: http://www.fiberhomegroup.com/en/articles/show-55-7.html.Search in Google Scholar
47. Functions of ONT and OLT in GPON Network. 2017. Available from: http://www.fiberoptical-networking.com/functions-ont-olt-gpon-network.html.Search in Google Scholar
48. ABC of PON: Understanding of OLT, ONU, ONT and ODN. 2015. Available from: https://community.fs.com/blog/abc-of-pon-understanding-olt-onuont-and-odn.html.Search in Google Scholar
49. Kaur, K, Kaur, R, Singh, R. Gigabit passive optical network- A review. Int J Advanced Res in Comput Commun Eng 2016;5:pp248–253. ITU-T recommendations G.984.7.Search in Google Scholar
50. IvicaCale, AS, Ivekovic, M. Gigabit passive optical network – GPON. In: IEEEInternational Conference on Information Technology Interfaces. Cavtat, Croatia: IEEE; 2007:679–84 pp.10.1109/ITI.2007.4283853Search in Google Scholar
51. Pesovic, A, Thomas, D. XGS-PON makes NG-PON simpler. 2016. Available from: https://insight.nokia.com/xgs-pon-makes-ng-pon-simpler.Search in Google Scholar
52. Barker, E. Software defined access: powering a 10 Gbps XGS-PON Network for 5G and More. 2017. Available from: http://about.att.com/innovationblog/voltha,Oct.05.Search in Google Scholar
53. Hajduczenia, M, da Silva, HJA. Next generation PON systems current status. Azores, Portugal: 11th International Conference on TransparentOptical Networks; 2009:1–8 pp.10.1109/ICTON.2009.5185097Search in Google Scholar
54. Chow, CW, Yeh, CH. Technology Advances for the 2nd Stage Next-Generation Passive-Optical-Network (NG-PON2). Hsinchu, Taiwan: IEEE International Conference on Advanced Infocomm Technology; 2013.10.1109/ICAIT.2013.6621496Search in Google Scholar
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