Authors : Archana Chauhan; Ranu Pandey

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/36jyuu8j

Scribd : https://tinyurl.com/y9mpsk6b

DOI : https://doi.org/10.38124/ijisrt/26apr2075

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The blistering development of Internet of Things (IoT) applications, smart healthcare, industrial automation, autonomous systems, immersive communications, has put high demands on the Quality of Service (QoS), including ultra-low latency, high reliability, high connectivity, and energy efficiency. Though the fifth-generation (5G) networks have partially met these needs, they cannot be fully relied upon in terms of intelligence, scalability, and adaptability of the future IoT ecosystems. The sixth-generation (6G) wireless networks will be projected as being AInative thus allowing autonomous, self-optimizing, and context-aware communication systems. In this survey, the author introduces a literature review of AI-driven methods of optimizing QoS of 6G-enabled IoT networks. We critically examine QoS needs of the emerging applications of IoT, important architectural characteristics of 6G networks, and application of artificial intelligence across various network layers. Research on different AI methods is discussed in terms of resource distribution, network slicing, traffic control, energy usage, and mobility. Moreover, this survey talks about widely utilized datasets, simulation, and performance assessment metrics applicable to AI-based optimization of QoS. Lastly, future directions and research challenges are brought to the fore and these are scalability, security, explainability, and real-world deployment issues. It is expected that this survey will offer researchers and practitioners a systematic review of the existing progress and will be used as a base to further research in the field of AI-enabled 6G IoT networks.

Keywords : Artificial Intelligence (AI), 6G Wireless Networks, Internet of Things (IoT), Quality of Service (QoS), QoS Optimization, Machine Learning, Network Slicing, Edge Intelligence.

References :

1. Z. Zhang, Y. Xiao, Z. Ma, et al., “6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies,” IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 28–41, Sep. 2019.
2. M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, “Disease Prediction by Machine Learning over Big Data from Healthcare Communities,” IEEE Access, vol. 5, pp. 8869–8879, 2017.
3. I. F. Akyildiz, A. Kak, and S. Nie, “6G and Beyond: The Future of Wireless Communications Systems,” IEEE Access, vol. 8, pp. 133995–134030, 2020.
4. S. Dang, O. Amin, B. Shihada, and M.-S. Alouini, “What Should 6G Be?,” Nature Electronics, vol. 3, pp. 20–29, 2020.
5. M. Tatipamula, D. A. Medhi, and B. D. Davie, “Quality of Service (QoS) in Internet of Things,” Computer Networks, vol. 128, pp. 172–185, 2017.
6. C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo, “Ma-chine Learning Paradigms for Next-Generation Wireless Networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98–105, Apr. 2017.
7. Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3072–3108, 2019.
8. H. Ye, G. Y. Li, and B.-H. Juang, “Deep Reinforcement Learning Based Resource Allocation for V2V Communications,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3163–3173, 2019.
9. N. Kato, Z. M. Fadlullah, B. Mao, F. Tang, and O. Akashi, “The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective,” IEEE Wireless Communications, vol. 24, no. 3, pp. 146–153, Jun. 2017.
10. X. Wang, Y. Han, V. Leung, et al., “Convergence of Edge Computing and Deep Learning: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 869–904, 2020.
11. M. Chiang, S. Ha, C.-L. I, and F. Risso, “Clarifying Fog Computing and Networking: 10 Questions and Answers,” IEEE Communications Maga-zine, vol. 55, no. 4, pp. 18–20, Apr. 2017.
12. Q. Zhang, Q. Chen, and Y. Li, “Intelligent Network Slicing for 6G Net-works: Architecture and Challenges,” IEEE Network, vol. 35, no. 4, pp. 203–209, 2021.
13. S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Federated Learning for Ultra-Reliable Low-Latency V2V Communications,” IEEE Trans-actions on Communications, vol. 68, no. 2, pp. 1146–1159, 2020.
14. K. Yang, S. Martin, and G. Zheng, “Energy-Efficient Wireless Communications: Tutorial, Survey, and Open Issues,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1508–1529, 2015.
15. F. Tang, Z. M. Fadlullah, B. Mao, and N. Kato, “An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 5141–5154, 2018.
16. Y. Liu, K. Wang, Y. Lin, and W. Xu, “Lightweight Deep Learning Models for Resource Allocation in 6G IoT Networks,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 123–135, 2022.
17. H. Tataria et al., “6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, Jul. 2021.
18. A. Gupta and R. K. Jha, “A Survey of 5G Network: Architecture and Emerging Technologies,” IEEE Access, vol. 3, pp. 1206–1232, 2015.
19. J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless Network Intelligence at the Edge,” Proceedings of the IEEE, vol. 107, no. 11, pp. 2204–2239, Nov. 2019.
20. P. Mach and Z. Becvar, “Mobile Edge Computing: A Survey on Architecture and Computation Offloading,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.
21. Y. Shi, K. Yang, T. Jiang, et al., “Communication-Efficient Federated Learning for Wireless Edge Intelligence,” IEEE Communications Maga-zine, vol. 58, no. 12, pp. 70–76, Dec. 2020.
22. A. Yadav and O. Kaiwartya, “AI-Enabled QoS-Aware Routing in IoT Net-works: A Survey,” Computer Communications, vol. 176, pp. 154–173, 2021.
23. R. Li, Z. Zhao, X. Zhou, et al., “Intelligent 5G: When Cellular Networks Meet Artificial Intelligence,” IEEE Wireless Communications, vol. 24, no. 5, pp. 175–183, Oct. 2017.
24. S. Zhang, P. Yang, and J. Zhang, “Digital Twin-Enabled Smart Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 809–848, 2022.
25. H. Elayan, O. Amin, B. Shihada, and M.-S. Alouini, “Terahertz Band: The Last Piece of RF Spectrum Puzzle for Communication Systems,” IEEE Open Journal of the Communications Society, vol. 1, pp. 1–32, 2020.
26. J. G. Andrews et al., “What Will 5G Be?,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1065–1082, 2014.
27. Y. Mao, C. You, J. Zhang, et al., “A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017.
28. X. Xu, H. Sun, and L. Qian, “AI-Driven Network Slicing for 6G IoT Net-works,” IEEE Network, vol. 36, no. 1, pp. 110–117, 2022.
29. M. Bennis, M. Debbah, and H. V. Poor, “Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale,” Proceedings of the IEEE, vol. 106, no. 10, pp. 1834–1853, Oct. 2018.
30. S. Latif, Z. Idrees, J. Ahmad, et al., “AI-Empowered 6G Networks: A Survey,” IEEE Access, vol. 9, pp. 155002–155022, 2021.