banner
Home > Open Journal Systems

An intelligent technique for network resource management and analysis of 5G-IoT smart healthcare application

Neha Gupta, Pradeep Kumar Juneja, Sachin Sharma, Umang Garg

Abstract


The transformation of traditional hospitals to a smart healthcare system is a patient-centric, reliable, and smarter way to enhance healthcare facilities for individuals. These healthcare devices are based on the Internet of Things (IoT) and conventional network structures such as 3rd generation (3G) or 4th generation (4G). That did not provide the patient health data in the real-time scenario. Therefore, the integrated 5G-IoT system offers distinct benefits such as remote monitoring, surgery, and real-time data analysis of healthcare system that is operated with slice of dedicated network. It generates huge data with the billions of healthcare equipment that can be evaluated for decision making. This enormous data requires low latency, high-security, capacity, and more reliable techniques for evaluation. In this scenario, network slicing is one of the key solutions that can be considered for isolated end-to-end network structures. Network slicing enables logical and independent network virtual networks to be multiplexed for the same physical network. In this article, we propose an intelligent approach for network resource optimization and analysis of a 5G-IoT based smart healthcare network. We examine the performance of the machine learning algorithm by an automatic model, named A Fast Library for automated machine learning (FLAML). In addition, we perform network slicing to obtain resource optimization in 5G-IoT-based healthcare application.


Keywords


healthcare; 5G; IoT; resource optimization; artificial intelligence; machine learning

Full Text:

PDF

References


1. Islam MM, Rahaman A, Islam MR. Development of smart healthcare monitoring system in IoT environment. SN Computer Science 2020; 1(3): 185. doi: 10.1007/s42979-020-00195-y

2. Ahad A, Tahir M, Yau KLA. 5G-based smart healthcare network: Architecture, taxonomy, challenges and future research directions. IEEE Access 2019; 7: 100747–100762. doi: 10.1109/ACCESS.2019.2930628

3. Brito JMC. Technological trends for 5G networks influence of E-Health and IoT applications. International Journal of E-Health and Medical Communications 2018; 9(1): 1–22. doi: 10.4018/IJEHMC.2018010101

4. Thayananthan V. Healthcare management using ICT and IoT based 5G. International Journal of Advanced Computer Science and Applications 2019; 10(4): 305–312. doi: 10.14569/ijacsa.2019.0100437

5. Najm AI, Hamoud AK, Lloret J, Bosch I. Machine learning prediction approach to enhance congestion control in 5G IoT environment. Electronics 2019; 8(6): 607. doi: 10.3390/electronics8060607

6. 5G in healthcare market. Available online: https://www.precedenceresearch.com/5g-in-healthcare-market (accessed on 13 July 2023).

7. Paramita S, Bebartta HND, Pattanayak P. IoT based healthcare monitoring system using 5G communication and machine learning models. In: Patgiri R, Biswas A, Roy P (editors). Health Informatics: A Computational Perspective in Healthcare. Springer Singapore; 2021. pp. 159–182.

8. Rghioui A, Lloret J, Sendra S, Oumnad A. A smart architecture for diabetic patient monitoring using machine learning algorithms. Healthcare 2020; 8(3): 348. doi: 10.3390/healthcare8030348

9. Lloret J, Parra L, Taha M, Tomás J. An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks 2017; 129: 340–351. doi: 10.1016/j.comnet.2017.05.018

10. Jain H, Chamola V, Jain Y, Naren. 5G network slice for digital real-time healthcare system powered by network data analytics. Internet of Things and Cyber-Physical Systems 2021; 1: 14–21. doi: 10.1016/j.iotcps.2021.12.001

11. Charleonnan A, Fufaung T, Niyomwong T, et al. Predictive analytics for chronic kidney disease using machine learning techniques. In: Proceedings of the 2016 Management and Innovation Technology International Conference (MITicon); 12–14 October 2016; Bang-San, Thailand. pp. MIT80–MIT83.

12. Wang Z, Won Chung J, Jiang X, et al. Machine learning-based prediction system for chronic kidney disease using associative classification technique. International Journal of Engineering & Technology 2018; 7(4): 1161–1167. doi: 10.14419/ijet.v7i4.36.25377

13. Yoo H, Han S, Chung K. A frequency pattern mining model based on deep neural network for real-time classification of heart conditions. Healthcare 2020; 8(3): 234. doi: 10.3390/healthcare8030234

14. Pradhan K, Chawla P. Medical Internet of things using machine learning algorithms for lung cancer detection. Journal of Management Analytics 2020; 7(4): 591–623. doi: 10.1080/23270012.2020.1811789

15. Zhang D, Rodrigues JJPC, Zhai Y, Sato T. Design and implementation of 5G E-Health systems: Technologies, use cases, and future challenges. IEEE Communications Magazine 2021; 59(9): 80–85. doi: 10.1109/mcom.001.2100035

16. Cheng X, Zhang C, Qian Y, et al. Editorial: Deep learning for 5G IoT systems. International Journal of Machine Learning and Cybernetics 2021; 12(11): 3049–3051. doi: 10.1007/s13042-021-01382-w

17. Ullah F, Naeem MR, Mostarda L, Shah SA. Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model. International Journal of Machine Learning and Cybernetics 2021; 12: 3115–3127. doi: 10.1007/s13042-020-01246-9

18. Meher P. Network slicing: 5G network slicing dataset according to 7 parameters. Available online: https://datasetsearch.research.google.com/search?query=5g&docid=L2cvMTF0Znc5dnh3Mw%3D%3D (accessed on 25 August 2023).

19. Subedi P, Alsadoon A, Prasad PWC, et al. Network slicing: A next generation 5G perspective. EURASIP Journal on Wireless Communications and Networking 2021; 102. doi: 10.1186/s13638-021-01983-7

20. Newaz AI, Sikder AK, Rahman MA, Uluagac AS. A survey on security and privacy issues in modern healthcare systems: Attacks and defenses. ACM Transactions on Computing for Healthcare 2020; 2(3): 27. doi: 10.1145/1122456

21. Alzaidi MS, Subbalakshmi C, Roshini TV, et al. 5G-telecommunication allocation network using IoT enabled improved machine learning technique. Wireless Communications and Mobile Computing 2022; 2022: 6229356. doi: 10.1155/2022/6229356

22. Hao Y, Tian D, Fortino G, et al. Network slicing technology in a 5G wearable network. IEEE Communications Standards Magazine 2018; 2(1): 66–71. doi: 10.1109/mcomstd.2018.1700083

23. Garcia-Aviles G, Gramaglia M, Serrano P, Banchs A. POSENS: A practical open source solution for end-to-end network slicing. IEEE Wireless Communications 2018; 25(5): 30–37. doi: 10.1109/MWC.2018.1800050

24. Sciancalepore V, Costa-Perez X, Banchs A. RL-NSB: Reinforcement learning-based 5G network slice broker. IEEE/ACM Transactions on Networking 2019; 27(4): 1543–1557. doi: 10.1109/tnet.2019.2924471




DOI: https://doi.org/10.32629/jai.v7i1.694

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Neha Gupta, Pradeep Kumar Juneja, Sachin Sharma, Umang Garg

License URL: https://creativecommons.org/licenses/by-nc/4.0/