Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

iMnet: Intelligent RAT Selection Framework for 5G Enabled IoMT Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The COVID-19 outburst has encouraged the adoption of Internet of Medical Things (IoMT) network to empower the antiquated healthcare system and alleviate the health care costs. To realise the functionalities of the IoMT network, 5G heterogeneous networks emerged as an exemplary connectivity solution as it facilitates diversified service provisioning in the service delivery model at more convenient care. However, the crucial challenge for 5G heterogeneous wireless connectivity solution is to facilitate agile differentiated service provisioning. Lately, considerable research endeavour has been noted in this direction but multiservice consideration and battery optimisation have not been addressed. Motivated by the gaps in the existing literature, an intelligent radio access technology selection approach has been proposed to ensure Quality of Service provisioning in a multiservice scenario on the premise of battery optimisation. In particular, the proposed approach leverages the concept of Double Deep Reinforcement Learning to attain an optimal network selection policy. Eventually, the proposed approach corroborated by the rigorous simulations demonstrated a substantial improvement in the overall system utility. Subsequently, the performance evaluation underlines the efficacy of the proposed scheme in terms of convergence and complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data Availability

This manuscript has no associated data file.

References

  1. Ugalmugale, S., Swain, R. (2020). Telemedicine market size by service (Tele-consulting, Tele-monitoring, Tele-education/training), by type (Telehospital, Telehome), by specialty (Cardiology, Gynecology, Neurology, Orthopedics, Dermatology, Mental Health), by delivery mode (Web/Mobile Telephonic, Visualized, Call Centers), Industry Analysis Report, Regional Outlook, Growth Potential, Price Trends, Competitive Market Share & Forecast, 2020–2026. Global Market Insights.https://www.gminsights.com/industry-analysis/telemedicine-market. Accessed September 26, 2020.

  2. Cisotto, G., Casarin, E., & Tomasin, S. (2020). Requirements and enablers of advanced healthcare services over future cellular systems. IEEE Communications Magazine, 58(3), 76–81. https://doi.org/10.1109/MCOM.001.1900349

    Article  Google Scholar 

  3. Desogus, C., Anedda, M., Murroni, M., & Muntean, G. M. (2019). A traffic type-based differentiated reputation algorithm for radio resource allocation during multi-service content delivery in 5G heterogeneous scenarios. IEEE Access, 7, 27720–27735.

    Article  Google Scholar 

  4. Monteiro, A., Souto, E., Pazzi, R., & Nogueira, M. (2019). Context-aware network selection in heterogeneous wireless networks. Computer Communications, 135, 1–15. https://doi.org/10.1016/j.comcom.2018.11.006

    Article  Google Scholar 

  5. Zhu, A., Guo, S., Liu, B., Ma, M., Feng, H., & Su, X. (2019). Adaptive multi-service heterogeneous network selection scheme in mobile edge computing. IEEE Internet of Things Journal, 6(4), 6862–6875. https://doi.org/10.1109/jiot.2019.2912155.

    Article  Google Scholar 

  6. Yadav, P., Agrawal, R., & Kashish, K. (2018). Heterogeneous network access for seamless data transmission in remote healthcare. International journal of grid and distributed computing, 11(8), 69–86.

    Article  Google Scholar 

  7. Arabi, S., Hammouti, H. E., Sabir, E., Elbiaze, H., & Sadik, M. (2019). RAT association for autonomic IoT systems. IEEE Network, 33(6), 1–8. https://doi.org/10.1109/mnet.2019.1800513

    Article  Google Scholar 

  8. Ma, M., Zhu, A., Guo, S., Wang, X., Liu, B., & Su, X. (2020). Heterogeneous network selection algorithm for novel 5G services based on evolutionary game. IET Communications, 14(2), 320–330. https://doi.org/10.1049/iet-com.2018.6290

    Article  Google Scholar 

  9. Alizadeh, A. & Vu, M. (2019). Early acceptance matching game for user association in 5G cellular HetNets. In: The Proceedings of IEEE Global Communications Conference (GLOBECOM). IEEE, Waikoloa, pp. 1-6, https://doi.org/10.1109/GLOBECOM38437.2019.9013871.

  10. Sandoval, R. M., Canovas-Carrasco, S., Garcia-Sanchez, A., & Garcia-Haro, J. (2019). A reinforcement learning-based framework for the exploitation of multiple RATs in the IoTd IEEE Access, 7, 123341–123354. https://doi.org/10.1109/ACCESS.2019.2938084

    Article  Google Scholar 

  11. Ding, H., Zhao, F., Tian, J., Li, D., & Zhang, H. (2019). A deep reinforcement learning for user association and power control in heterogeneous networks. Ad Hoc Networks, 102, 1–18.

    Google Scholar 

  12. Mollel, M. S., Abubakar, A. I., Ozturk, M., Kaijage, S., Kisangiri, M., Zoha, A., & Abbasi, Q. H. (2020). Intelligent handover decision scheme using double deep reinforcement learning. Physical Communication, 42(2020), 1–12. https://doi.org/10.1016/j.phycom.2020.101133

    Article  Google Scholar 

  13. Ma, M., Zhu, A., Guo, S., & Yang, Y. (2021). Intelligent network selection algorithm for multiservice users in 5G heterogeneous network system: Nash Q-learning method. IEEE Internet of Things Journal, 8(15), 11877–11890. https://doi.org/10.1109/JIOT.2021.3073027

    Article  Google Scholar 

  14. Chkirbene, Z., Awad, A., Mohamed, A., Erbad, A., & Guizani, M. (2021). Deep reinforcement learning for network selection over heterogeneous health systems. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2021.3058037

    Article  Google Scholar 

  15. Arabi, S., Hammouti Hel, Sabir, E., Elbiaze, H., Sadik, M. (2018). Lightweight energy-cost-efficient RAT association for Internet of Things. In: The Proceedings of 4th World Forum on Internet of Things (WF-IoT). IEEE, Singapore, pp. 332–337. https://doi.org/10.1109/WF-IoT.2018.8355135.

  16. Giordani, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2018). An efficient uplink multi-connectivity scheme for 5G millimeter-wave control plane applications. IEEE Transactions on Wireless Communications, 17(10), 6806–6821. https://doi.org/10.1109/twc.2018.2864650

    Article  Google Scholar 

  17. Priya, B., & Malhotra, J. (2021). 5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03606-x

  18. Priya, B., & Malhotra, J. (2020). QAAs: QoS provisioned artificial intelligence framework for AP selection in next-generation wireless networks. Telecommunication System. https://doi.org/10.1007/s11235-020-00710-9

  19. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.

    MATH  Google Scholar 

  20. Hasselt, H.V., Guez, A. & Silver, D. (2016). Deep Reinforcement Learning with Double Q-Learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoneix, pp 2094-2100.

  21. Johnson, A., Pollard, T. & Mark, R. (2019). MIMIC-III clinical database demo (version 1.4). PhysioNet. https://doi.org/10.13026/C2HM2Q.

  22. Matlab (2021). Deep Learning Toolbox Design, train, and analyze deep learning networks. https://www.mathworks.com/products/deep-learning.html. Accessed March 8, 2022.

  23. Serpen, G., & Gao, Z. (2014). Complexity analysis of multilayer perceptron neural network embedded into a wireless sensor network. Procedia Computer Science, 36, 192–197. https://doi.org/10.1016/j.procs.2014.09.078

    Article  Google Scholar 

  24. Zhang, Q., Liang, Y.-C., & Poor, H. V. (2020). Intelligent user association for symbiotic radio networks using deep reinforcement learning. IEEE Transactions on Wireless Communications, 19(7), 4535–4548. https://doi.org/10.1109/TWC.2020.2984758

    Article  Google Scholar 

  25. Zhang, Q., Lin, M., Yang, L. T., Chen, Z., Khan, S. U., & Li, P. (2018). A double deep Q-learning model for energy-efficient edge scheduling. IEEE Transactions on Services Computing, 12(5), 739–749.

    Article  Google Scholar 

  26. Mismar, F.B., Evans, B.L. (2018). Deep Q-learning for self-organizing networks fault management and radio performance improvement. In: Proceedings of 52nd Asilomar Conference on Signals, Systems, and Computers—Pacific Grove,IEEE, CA, pp 1457–1461.

  27. Efroni, Y., Merlis, N., Ghavamzadeh, M., Mannor, S. (2019). Tight regret bounds for model-based reinforcement learning with greedy policies. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, pp 12224–12234.

  28. Zhang, Q., Liang, Y.-C., & Poor, H. V. (2020). Intelligent user association for symbiotic radio networks using deep reinforcement learning. IEEE Transactions on Wireless Communications, 19(7), 4535–4548. https://doi.org/10.1109/TWC.2020.2984758

    Article  Google Scholar 

  29. Zhao, N., Liang, Y. C., Niyato, D., Pei, Y., Wu, M., & Jiang, Y. (2019). Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 18(11), 5141–5152.

    Article  Google Scholar 

  30. Bhattacharya, R., et al. (2019). QFlow: A reinforcement learning approach to high QoE video streaming over wireless networks. In: Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing, ACM. Catania, pp. 251–260.

Download references

Acknowledgements

Author would like to thank University Grant Commission, New Delhi for Junior Research Fellowship.

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Engineering and Technology, GNDU RC, Jalandhar, India

    Bhanu Priya & Jyoteesh Malhotra

Authors
  1. Bhanu Priya
    View author publications

    Search author on:PubMed Google Scholar

  2. Jyoteesh Malhotra
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Bhanu Priya.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest, financial or otherwise.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Priya, B., Malhotra, J. iMnet: Intelligent RAT Selection Framework for 5G Enabled IoMT Network. Wireless Pers Commun 129, 911–932 (2023). https://doi.org/10.1007/s11277-022-10163-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-10163-9

Keywords

Profiles

  1. Bhanu Priya View author profile
  2. Jyoteesh Malhotra View author profile