Skip to main content

Advertisement

Log in

Blockchain-based IoT enabled health monitoring system

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Health monitoring systems are improving with the development of the internet of things. This paper proposes a secure architecture consisting of a four-layer internet of things enabled health monitoring system that collects patient data and classifies them into different medical categories. While collecting patient information from their wearable smart sensing devices for computation, the privacy and security of this process are essential. The main motive of this paper is to develop a lightweight and secure communication protocol using blockchain architecture for decentralized IoT networks and classify them into different categories using transfer learning. We propose a framework that uses blockchain for security and incorporates transfer learning to use multiple pre-trained models. The proposed routing technique uses factors like probability, credibility rating, and node energy to route the data to its destination such that the network overhead is reduced and the energy used is minimal. We classify the collected patient information using four different pre-trained convolutional neural network models: ResNet50, VGG19, InceptionV3, and SqueezeNet. We simulate the proposed routing approach and other benchmark schemes on various performance metrics. The results show that the proposed approach gives 92.24% classification accuracy.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. For more details on dataset, please refer to the paper [13].

References

  1. Becker VD, Vahdat A (2000) Epidemic routing for partially connected ad hoc networks. In: Proceedings of technique report. Department of Computer Science, Duke University, Durham, UK

  2. Chamarajnagar R, Ashok A (2018) Opportunistic mobile IoT with blockchain based collaboration. In: 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 1–6

  3. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848

  4. Dorri A, Kanhere SS, Jurdak R (2017a) Towards an optimized blockchain for IoT. In: 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE, pp 173–178

  5. Dorri A, Kanhere SS, Jurdak R, Gauravaram P (2017b) Blockchain for IoT security and privacy: The case study of a smart home. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, pp 618–623

  6. Dorri A, Kanhere SS, Jurdak R, Gauravaram P (2019) LSB: a lightweight scalable blockchain for IoT security and anonymity. J Parallel Distrib Comput 134:180–197

    Article  Google Scholar 

  7. Faruk MJH, Shahriar H, Valero M, Sneha S, Ahamed SI, Rahman M (2021) Towards blockchain-based secure data management for remote patient monitoring. In: 2021 IEEE International Conference on Digital Health (ICDH). IEEE, pp 299–308

  8. Fotiou N, Pittaras I, Siris VA, Polyzos GC (2019) Enabling opportunistic users in multi-tenant IoT systems using decentralized identifiers and permissioned blockchains. In: Proceedings of the 2nd international ACM workshop on security and privacy for the Internet-of-Things, pp 22–23

  9. Hameed K, Garg S, Amin MB, Kang B (2021) A formally verified blockchain-based decentralised authentication scheme for the internet of things. J Supercomput 77(12):14461–14501

    Article  Google Scholar 

  10. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

  11. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(< 0.5\) mb model size. arXiv:1602.07360

  12. Kazmi HSZ, Nazeer F, Mubarak S, Hameed S, Basharat A, Javaid N (2019) Trusted remote patient monitoring using blockchain-based smart contracts. In: International Conference on Broadband and Wireless Computing. Springer, Communication and Applications, pp 765–776

  13. Khamparia A, Singh PK, Rani P, Samanta D, Khanna A, Bhushan B (2021) An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning. Trans Emerg Telecommun Technol 32(7):e3963

    Google Scholar 

  14. Khanna A, Rani P, Sheikh TH, Gupta D, Kansal V, Rodrigues JJ (2021) Blockchain-based security enhancement and spectrum sensing in cognitive radio network. Wirel Pers Commun pp 1–23

  15. Lindgren A, Doria A, Schelén O (2003) Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mob Comput Commun Rev 7(3):19–20

    Article  Google Scholar 

  16. Loreti P, Bracciale L (2019) Optimized neighbor discovery for opportunistic networks of energy constrained IoT devices. IEEE Trans Mob Comput 19(6):1387–1400

    Article  Google Scholar 

  17. Mohanty SN, Ramya K, Rani SS, Gupta D, Shankar K, Lakshmanaprabu S, Khanna A (2020) An efficient lightweight integrated blockchain (ELIB) model for IoT security and privacy. Futur Gener Comput Syst 102:1027–1037

    Article  Google Scholar 

  18. Pan J, Wang J, Hester A, Alqerm I, Liu Y, Zhao Y (2018) EdgeChain: an edge-IoT framework and prototype based on blockchain and smart contracts. IEEE Internet Things J 6(3):4719–4732

    Article  Google Scholar 

  19. Ramezan G, Leung C (2018) A blockchain-based contractual routing protocol for the Internet of Things using smart contracts. Wirel Commun Mob Comput 2018

  20. Rani P, Bhambay R (2022) A comparative survey of consensus algorithms based on proof-of-work. In: 3rd International Conference on Emerging Technologies in Data Mining and Information Security. Springer

  21. Rani P, Balyan A, Jain V, Sangwan D, Singh PP, Shokeen J (2020) A probabilistic routing-based secure approach for opportunistic IoT network using blockchain. In: 2020 IEEE 17th India Council International Conference (INDICON). IEEE, pp 1–7

  22. Rani P, Jain V, Joshi M, Khandelwal M, Rao S (2021a) A secured supply chain network for route optimization and product traceability using blockchain in internet of things. In: Data analytics and management. Springer, pp 637–647

  23. Rani P, Singh PP, Balyan A, Shokeen J, Jain V, Sangwan D (2021b) A secure epidemic routing using blockchain in opportunistic internet of things. In: Data analytics and management. Springer, pp 101–110

  24. Rathore S, Kwon BW, Park JH (2019) BlockSecIoTNet: blockchain-based decentralized security architecture for IoT network. J Netw Comput Appl 143:167–177

    Article  Google Scholar 

  25. Rathore S, Pan Y, Park JH (2019) BlockDeepNet: a blockchain-based secure deep learning for IoT network. Sustainability 11(14):3974

    Article  Google Scholar 

  26. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  27. Sujitha B, Parvathy VS, Lydia EL, Rani P, Polkowski Z, Shankar K (2021) Optimal deep learning based image compression technique for data transmission on industrial internet of things applications. Trans Emerg Telecommun Technol 32(7):e3976

    Google Scholar 

  28. Sun Y, Zhang L, Feng G, Yang B, Cao B, Imran MA (2019) Blockchain-enabled wireless Internet of Things: performance analysis and optimal communication node deployment. IEEE Internet Things J 6(3):5791–5802

    Article  Google Scholar 

  29. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2818–2826

  30. Tschandl P, Rosendahl C, Kittler H (2018) The ham 10,000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5(1):1–9

    Article  Google Scholar 

  31. Waheed U, Khan MSA, Awan SM, Khan MA, Mansoor Y (2019) Decentralized approach to secure IoT based networks using blockchain technology. 3C Tecnología Glosas de innovación aplicadas a la pyme pp 182–205

  32. Xie J, Tang H, Huang T, Yu FR, Xie R, Liu J, Liu Y (2019) A survey of blockchain technology applied to smart cities: research issues and challenges. IEEE Commun Surv Tutor 21(3):2794–2830

    Article  Google Scholar 

  33. Yang R, Yu FR, Si P, Yang Z, Zhang Y (2019) Integrated blockchain and edge computing systems: a survey, some research issues and challenges. IEEE Commun Surv Tutor 21(2):1508–1532

    Article  Google Scholar 

Download references

Acknowledgements

This research did not receive any specific grant from funding agencies in the commercial, public, or non-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jyoti Shokeen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Rani, P., Kaur, P., Jain, V. et al. Blockchain-based IoT enabled health monitoring system. J Supercomput 78, 17284–17308 (2022). https://doi.org/10.1007/s11227-022-04584-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04584-3

Keywords

Navigation