Skip to main content

Advertisement

SpringerLink
Log in

Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The diabetes is a critical disease from the small children to old age people. Due to improper diet and physical activities of the living population, obesity becomes prevalent in young generation. If we analyze self care of individual life, no man or women ready to spend their time for health care. It leads to problem like diabetes, blood pressure etc. Today is a busy world were robots and artificial machines ready to take care of human personal needs. Automatic systems help humans to manage their busy schedule. It motivates us to develop a diabetes motoring system for patients using IoT device in their body which monitors their blood sugar level, blood pressure, sport activities, diet plan, oxygen level, ECG data. The data are processed using feature selection algorithm called as particle swarm optimization and transmitted to nearest edge node for processing in 5G networks. Secondly, data are processed using DBN Layer. Thirdly, we share the diagnosed data output through the wireless communication such as LTE/5G to the patients connected through the edge nodes for further medical assistance. The patient wearable devices are connected to the social network. The Result of our proposed system is evaluated with some existing system. Time and Performance outperform than other techniques.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Chen M et al (2017) Green and mobility-aware caching in 5G networks. IEEE Trans Wirel Commun 16(12):8347–8361

    Article  Google Scholar 

  2. Sharkawy R, Ibrahim K, Salama MMA, Bartnikas R (2011) Particle swarm optimization feature selection for the classification of conducting particles in transformer oil. IEEE Trans Dielectr Electr Insul 18(6):1897–1907

    Article  Google Scholar 

  3. Srinivasan D, Seow TH (2005) Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for multi-criteria optimization problems. In: Abraham A, Jain L, Goldberg R (eds) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-137-7_7

  4. Algarsamy A , Kathavarayan RS (2018) Classification with modified deep belief network for large dataset. Int J Eng Res Technol 11(10):1511-1524. ISSN 0974-3154

  5. Tamilselvan P, Wang Y, Wang P, Sky B (2012) Deep Belief Network based state classification for structural health diagnosis. 2012 IEEE Aerospace Conference, Big Sky, MT, pp 1-11. https://doi.org/10.1109/AERO.2012.6187366

  6. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554

    Article  MathSciNet  Google Scholar 

  7. Hinton G (2010) A practical guide to training restricted Boltzmann machines. Momentum 9:1

  8. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems, p 153–160

  9. Chen M, Yang J, Zhou J, Hao Y, Zhang J, Youn C (2018) 5G-Smart diabetes: toward personalized diabetes diagnosis with healthcare big data clouds. IEEE Commun Mag 56(4):16–23. https://doi.org/10.1109/MCOM.2018.1700788

    Article  Google Scholar 

  10. Amine Rghioui J, Lloret SS, Oumnad A (2020) A smart architecture for diabetic patient monitoring using machine learning algorithms. Healthcare 8:348. https://doi.org/10.3390/healthcare8030348

    Article  MATH  Google Scholar 

  11. Ahad A, Tahir M, Yau KA (2019) 5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions. IEEE Access 7:100747–100762. https://doi.org/10.1109/ACCESS.2019.2930628

    Article  Google Scholar 

  12. Ahad A, Tahir M, Sheikh MA,  Ahmed K, Mughees A, Numani A (2020)  Technologies trend towards 5G network for smart health-care using IoT: a review. Sensors 20:4047. https://doi.org/10.3390/s20144047

  13. Mahmoud MM, Rodrigues JJ, Ahmed SH, Shah SC, Al-Muhtadi JF, Korotaev VV, De Albuquerque VHC (2018) Enabling technologies on cloud of things for smart healthcare. IEEE Access 6:31950–31967

    Article  Google Scholar 

  14. Baker SB, Xiang W, Atkinson I (2017) Internet of things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access 5:26521–26544

    Article  Google Scholar 

  15. Qi J, Yang P, Min G, Amft O, Dong F, Xu L (2017) Advanced internet of things for personalised healthcare systems: A survey. Pervasive Mob Comput 41:132–149

    Article  Google Scholar 

  16. Lloret J, Parra L, Taha M, Tomás J (2017) An architecture and protocol for smart continuous eHealth monitoring using 5G. Comput Netw 129:340–351

    Article  Google Scholar 

  17. Xiao F, Miao Q, Xie X, Sun L, Wang R (2018) Indoor anti-collision alarm system based on wearable Internet of Things for smart healthcare. IEEE Commun Mag 56:53–59

    Article  Google Scholar 

  18. Yoo H, Han S, Chung KA (2020) Frequency pattern mining model based on deep neural network for real-time classification of heart conditions. Healthcare 8:234

  19. González-Valenzuela S, Chen M, Leung VCM (2011) Mobility support for health monitoring at home using wearable sensors. IEEE Trans Inf Technol Biomed 15:539–549

    Article  Google Scholar 

  20. Abd El-Latif AA, Abd-El-Atty B, Mehmood I, Muhammad K, Venegas-Andraca SE, Peng J (2021) Quantum-inspired blockchain-based cybersecurity: securing smart edge utilities in IoT-based smart cities. Inf Process Manag 58(4):102549

    Article  Google Scholar 

  21. Zhang WZ, Elgendy IA, Hammad M, Iliyasu AM, Du X, Guizani M, El-Latif A (2020) Secure and optimized load balancing for multitier IoT and edge-cloud computing systems. IEEE Internet Things J 8(10):8119–8132

    Article  Google Scholar 

  22. Xu M, Peng J, Gupta BB, Kang J, Xiong Z, Li Z, Abd El-Latif AA (2021) Multi-agent federated reinforcement learning for secure incentive mechanism in intelligent cyber-physical systems. IEEE Internet Things J 16–23. https://doi.org/10.1109/JIOT.2021.3081626

  23. Elgendy IA, Zhang WZ, He H, Gupta BB, Abd El-Latif AA (2021) Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms. Wirel Netw 27(3):2023–2038

    Article  Google Scholar 

  24. Eberhart R, Kennedy J (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R234), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

  1. Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic

    K. Venkatachalam & S. Hubálovský

  2. Department of Computer Sceince, CHRIST (Deemed to be University), Bangalore, 560074, India

    P. Prabu

  3. Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Ala Saleh Alluhaidan

  4. Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003, Hradec Králové, Czech Republic

    P. Trojovský

Authors
  1. K. Venkatachalam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Prabu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ala Saleh Alluhaidan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Hubálovský
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Trojovský
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ala Saleh Alluhaidan.

Ethics declarations

Conflicts of interest/Competing interests

Enclosed separate disclosure.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Venkatachalam, K., Prabu, P., Alluhaidan, A.S. et al. Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT. Mobile Netw Appl 27, 1060–1069 (2022). https://doi.org/10.1007/s11036-021-01861-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-021-01861-y

Keywords

Search

Navigation