A Decoupled Blockchain Approach for Edge-envisioned IoT-based Healthcare Monitoring

Aujla, Gagangeet Singh; Jindal, Anish

Abstract

The in-house health monitoring sensors form a large network of Internet of things (IoT) that continuously monitors and sends the data to the nearby devices or server. However, the connectivity of these IoT-based sensors with different entities leads to security loopholes wherein the adversary can exploit the vulnerabilities due to the openness of the data. This is a major concern especially in the healthcare sector where the change in data values from sensors can change the course of diagnosis which can cause severe health issues. Therefore, in order to prevent the data tempering and preserve the privacy of patients, we present a decoupled blockchain-based approach in the edge-envisioned ecosystem. This approach leverages the nearby edge devices to create the decoupled blocks in blockchain so as to securely transmit the healthcare data from sensors to the edge nodes. The edge nodes then transmit and store the data at the cloud using the incremental tensor-based scheme. This helps to reduce the data duplication of the huge amount of data transmitted in the large IoT healthcare network. The results show the effectiveness of the proposed approach in terms of the block preparation time, header generation time, tensor reduction ratio, and approximation error.

Citation

Aujla, G. S., & Jindal, A. (2021). A Decoupled Blockchain Approach for Edge-envisioned IoT-based Healthcare Monitoring. IEEE Journal on Selected Areas in Communications, 39(2), 491-499. https://doi.org/10.1109/jsac.2020.3020655

• Journal Article Type: Article
• Online Publication Date: Sep 3, 2020
• Publication Date: 2021-02
• Deposit Date: Sep 29, 2020
• Publicly Available Date: Nov 8, 2021
• Journal: IEEE Journal on Selected Areas in Communications.
• Print ISSN: 0733-8716
• Electronic ISSN: 1558-0008
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 39
• Issue: 2
• Pages: 491-499
• DOI: https://doi.org/10.1109/jsac.2020.3020655
• Public URL: https://durham-repository.worktribe.com/output/1291467