BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain
Open accessAbstract
The healthcare industry has witnessed significant transformations in e-health services by using mobile-edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low Quality of Services (QoS), data privacy, and system security vulnerabilities. In this article, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data-sharing scheme, which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contract-based authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.
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Offloading health data to the cloud for execution incurs excessive network latency, especially when medical data have large sizes.
“In the traditional AI-based smart healthcare systems, the offloading of health data to the cloud for execution incurs excessive network latency [29], especially when medical data often have large sizes (e.g., audio, images).”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [29]
Blockchain can provide solutions for FL-based edge computing due to decentralization, immutability, and traceability.
“Fortunately, blockchain as a ledger technology [6] can provide attractive solutions for FL-based intelligent edge computing due to its unique features such as decentralization, immutability, and traceability.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [6]
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Self-citation
- Authors
- Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Deakin University (AU)
- UNSW Sydney (AU)
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-07-15
- Volume
- 8
- Issue
- 14
- Pages
- 11743-11757
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3129674742
- Cited by
- 184
- References
- 32
- Citations by year
- 2026: 16 · 2025: 33 · 2024: 39 · 2023: 45 · 2022: 35 · 2021: 16
- FWCI
- 15.14
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (cc-by)
- Funders
- CSIRO Data61, Australia
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- mobile edge computing, blockchain, Internet of Medical Things, decentralized architecture, data offloading, data sharing, smart contracts, authentication, IPFS, data privacy, hospital networks, quality of service
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