Integration of AI in passive optical networks (PON): enhancing performance and efficiency
-
Amit Kumar
,
Rajni Rani
und
Ashok Kumar
Abstract
Passive Optical Networks (PON) have emerged as a foundational technology in modern broadband infrastructure, renowned for their ability to deliver high-speed, high-bandwidth communication with low energy consumption and long-term scalability. However, as digital services expand and network traffic grows exponentially – driven by the proliferation of cloud computing, real-time applications, IoT devices, and video streaming – the limitations of traditional PON architectures are becoming increasingly evident. Static network configurations and conventional management strategies struggle to adapt to fluctuating demands, ensure consistent Quality of Service (QoS), and respond swiftly to faults or security threats. In response to these challenges, Artificial Intelligence (AI) offers a promising solution by introducing automation, adaptability, and data-driven intelligence into optical networks. This paper explores the integration of AI techniques into PON systems across key application domains, including fault detection, traffic forecasting, dynamic resource allocation, QoS optimization, and cyber security enhancement. By leveraging AI, PONs can achieve improved operational efficiency, predictive maintenance, enhanced user experience, and robust security, setting the foundation for intelligent and self-optimizing next-generation access networks. The study also discusses performance metrics, visual representations, challenges, and future directions to guide ongoing research and practical implementations.
Acknowledgements
Thanks to all my co author for the support.
-
Research ethics: Not applicable.
-
Informed consent: We all are fully responsible for this paper.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The author states no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
1. Sharma, V, Sukhroop, P, Kumar, S, Rani, R. A comparative study of routing protocols, AI, and passive optical networks in the evolution of mobile ad hoc networks (MANETS). J Opt Commun 2025. https://doi.org/10.1515/joc-2024-0319.Suche in Google Scholar
2. Sharma, V, Sukhroop, P, Kumar, S, Rani, R. Smart connectivity in motion: high-speed optical backhaul for mobile ad hoc networks. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0037.Suche in Google Scholar
3. Sharma, V, Sukhroop, P, Rani, R, Kumar, S. Dynamic routing in Mobile Ad-Hoc networks using AI-Powered optical waveguides. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0074.Suche in Google Scholar
4. Rani, R, Tyagi, S, Sharma, V. Integration of IOT and optical networks for enhanced connectivity and performance. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0118.Suche in Google Scholar
5. Sharma, V, Sharma, S. Performance enhancement of passive optical network with improved bandwidth utilisation and allocation. J Opt Commun 2024;45:s1279–85. https://doi.org/10.1515/joc-2022-0341.Suche in Google Scholar
6. Sharma, V, Sukhroop, P, Rani, R, Kumar, S. An hybrid approach to MANET routing: leveraging optical networks and next-gen innovations. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0064.Suche in Google Scholar
7. Sukhroop, P, Bhardwaj, V, Sharma, V, Rani, R, Kumar, S. A passive optical network-approach for multiaccess edge computing optimization. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0174.Suche in Google Scholar
8. Mishra, D, Tyagi, S, Sharma, V, Mishra, V. A revolutionary framework for cloud security: enhancing data protection during transmission and migration over optical networks. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0158.Suche in Google Scholar
9. Kumar, N, Garg, SK, Tyagi, S, Sharma, V. Evaluation and analysis of passive optical network in investigating real-time cell phone detection in restricted zones. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0205.Suche in Google Scholar
10. kumar, A, Singh, AK, Yadav, A, Sharma, S, Sharma, V. Evaluation and analysis of passive optical network in investigating drilling time relative to material thickness. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0191.Suche in Google Scholar
11. Sharma, V, Kumar, S, Bhardwaj, V. Optimizing conventional machining process parameters for A713 aluminum alloy using taguchi method and passive optical network for data transmission. J Opt Commun 2024. https://doi.org/10.1515/joc-2024-0257.Suche in Google Scholar
12. Ashima, Kishor, A, Agrawal, R, Kumar, T, Sharma, V. Optimized heuristic LEACH variants for energy-efficient routing in passive optical networks. J Opt Commun 2025. https://doi.org/10.1515/joc-2025-0241.Suche in Google Scholar
13. Sharma, V, Sharma, S, Kumar, A. Passive optical network: a new approach in optical network. In: 2020 international conference on advances in computing, communication & materials (ICACCM). Dehradun, India; 2020:295–300 pp.10.1109/ICACCM50413.2020.9213059Suche in Google Scholar
14. Sharma, V, Sharma, S. Evaluation and analysis of passive optical network with optimum parameter’s. In: 2021 international conference on advances in electrical, computing, communication and sustainable technologies (ICAECT). Bhilai, India; 2021:1–6 pp.10.1109/ICAECT49130.2021.9392408Suche in Google Scholar
15. Sharma, V, Sharma, S. Passive optical networks: a futuristic approach. Integrated Research Advances 2018;5:30–5.Suche in Google Scholar
16. Sharma, V, kumar kamal kumar Sharma, R. Analysis the fir filter using a adaptive techniques method. In: International journal of institutional and industrial research; 2017:10–12 pp.Suche in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
