Skip to main content

Advertisement

Log in

Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems

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

Abstract

Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usually record the patient data and transfer it to the cloud for further processing. Energy efficiency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an effective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artificial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efficient data transmission in IoT networks. The IoT devices are primarily used to collect patients’ medical data. The AIBS- IoTH model involves a metaheuristic-based modified sunflower optimization-based clustering (MSFOC) technique to achieve energy efficiency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra-cluster communication. At last, the Classification Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Alqaralleh BA, Vaiyapuri T, Parvathy VS, Gupta D, Khanna A, Shankar K (2021) Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things environment. Personal and Ubiquitous Computing, pp 1–11

  2. Dwivedi AD, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2):326

    Article  Google Scholar 

  3. Abdolkhani R, Gray K, Borda A, DeSouza R (2019) Patient-generated health data management and quality challenges in remote patient monitoring. JAMIA Open (ooz036). https://doi.org/10.1093/jamiaopen/ooz036

  4. Veeramakali T, Siva R, Sivakumar B, Mahesh PS, Krishnaraj N (2021) An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model. The Journal of Supercomputing 77:9576–9596

  5. Uddin MA, Stranieri A, Gondal I, Balasubramanian V (2018) Continuous patient monitoring with a patient centric agent: a block architecture. IEEE Access 6:32700–32726

    Article  Google Scholar 

  6. Uddin MA, Stranieri A, Gondal I, Balasubramanian (2018) A patient agent to manage blockchains for remote patient monitoring. Stud Health Technol Inform 254:105–115

    Google Scholar 

  7. Tuli S, Mahmud R, Tuli S, Buyya R (2018) Fogbus: a blockchain-based lightweight framework for edge and fog computing, arXiv: 1811.11978

  8. Li Z-t, Chen Q, Zhu G-m, Choi Y-j, Sekiya H (2015) A low latency, energy efficient MAC protocol for wireless sensor networks. Int J Distrib Sens Netw 11(8):1–9

    Article  Google Scholar 

  9. Wang W, Garofalakis M, Ramchandran K (2007) Distributed sparse random projection for refinable approximation, IEEE 6th International Symposium on Information Processing in Sensor Networks, pp 331–339

  10. Ullah F, Ullah I, Khan A, Uddin MI, Alyami H, Alosaimi W (2020) Enabling clustering for privacy-aware data dissemination based on Medical Healthcare-IoTs (MH-IoTs) for wireless body area network. Journal of Healthcare Engineering 2020:1–10

  11. Shukla S, Thakur S, Hussain S, Breslin JG, Jameel SM (2021) Identification and authentication in healthcare Internet-of-Things using integrated fog computing based blockchain model. Internet of Things 15:100422

  12. Dwivedi AD, Malina L, Dzurenda P, Srivastava G (2019) Optimized blockchain model for internet of things based healthcare applications. In: 2019 42nd international conference on telecommunications and signal processing (TSP). IEEE, pp 135–139

  13. Shynu PG, Menon VG, Kumar RL, Kadry S, Nam Y (2021) Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing. IEEE Access 9:45706–45720

    Article  Google Scholar 

  14. Guo X, Lin H, Wu Y, Peng M (2020) A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Futur Gener Comput Syst 113:407–417

    Article  Google Scholar 

  15. Honar Pajooh H, Rashid M, Alam F, Demidenko S (2021) Multi-layer blockchain-based security architecture for internet of things. Sensors 21(3):772

  16. Kumar R, Tripathi R (2021) Towards design and implementation of security and privacy framework for internet of medical things (iomt) by leveraging blockchain and ipfs technology. The Journal of Supercomputing 77:7916–7955

  17. Hossein KM, Esmaeili ME, Dargahi T (2019) Blockchain-based privacy-preserving healthcare architecture. In: 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, pp 1–4

  18. Bhattacharya P, Mehta P, Tanwar S, Obaidat MS, Hsiao KF (2020) HeaL: A blockchain-envisioned signcryption scheme for healthcare IoT ecosystems. In: 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). IEEE, pp 1–6

  19. Alzubi JA (2021) Blockchain-based Lamport Merkle Digital signature: authentication tool in IoT healthcare. Comput Commun 170:200–208

    Article  Google Scholar 

  20. Ray PP, Chowhan B, Kumar N, Almogren A (2021) BIoTHR: Electronic health record servicing scheme in IoT-blockchain ecosystem. IEEE Internet of Things Journal 8(13):10857–10872

  21. Frikha T, Chaari A, Chaabane F, Cheikhrouhou O, Zaguia A (2021) Healthcare and fitness data management using the IoT-based blockchain platform. Journal of Healthcare Engineering 2021:1–12

  22. Zaabar B, Cheikhrouhou O, Jamil F, Ammi M, Abid M (2021) HealthBlock: A secure blockchain-based healthcare data management system. Comput Netw 200:108500

  23. Mansour RF (2022) Blockchain assisted clustering with Intrusion Detection System for Industrial Internet of Things environment. Expert Systems with Applications 207:117995

  24. Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249

  25. Gomes GF, da Cunha SS, Ancelotti AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626

    Article  Google Scholar 

  26. Nguyen TT (2021) Enhanced sunflower optimization for placement distributed generation in distribution system. Int J Electr Comput Eng 11(1):107

  27. Suh S, Lee H, Lukowicz P, Lee YO (2021) CEGAN: classification enhancement generative adversarial networks for unraveling data imbalance problems. Neural Netw 133:69–86

    Article  Google Scholar 

  28. Nguyen GN, Le Viet NH, Devaraj AFS, Gobi R, Shankar K (2020) Blockchain enabled energy efficient red deer algorithm based clustering protocol for pervasive wireless sensor networks. Sustain Comput Inform Syst 28:100464

  29. Dua D, and Graff C (2019) UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science

  30. Bhuvaneeswari R, Sudhakar P, Prabakaran G (2019) Heart disease prediction model based on gradient boosting tree (GBT) classification algorithm. Int J Recent Technol Eng 8(2):41–51

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José Escorcia-Gutierrez.

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

Escorcia-Gutierrez, J., Mansour, R.F., Leal, E. et al. Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02115-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-023-02115-9

Keywords

Navigation