Jing Yang, Abdullah Ayub Khan, Diva Kurnianingtyas, Muh Arif Rahman, Sunil Prajapat, Por Lip Yee
Recent developments in blockchain technology (BT) have greatly increased its potential to guarantee provenance, transparency, data quality, and trustworthiness, especially in the healthcare industry. However, enduring issues such as scalability, privacy protection, resilience, elasticity, and security concerns continue to impede its adoption in healthcare systems. The evolution toward 6G-ready edge healthcare networks further amplifies these challenges, demanding low-latency, high-reliability, and secure data interoperability across distributed medical institutions. This paper explores the critical role of Hyperledger technology in enabling efficient and interoperable health data management systems within this emerging context. Specifically, we focus on Hyperledger Sawtooth for its ability to support compliant protocol development and regulatory framework alignment with government standards. We propose a consortium blockchain network equipped with two dedicated intercommunication channels to facilitate secure and seamless health data exchange, organization, and preservation at the edge. All transactions employ SHA-256 hash encryption and shared public keys, ensuring enhanced provenance, security, and privacy during data transmission. To achieve cost efficiency and frictionless data sharing among healthcare stakeholders, a customizable Proof of Elapsed Time (PoET) consensus mechanism is introduced. The proposed framework, termed “BHI-5”, is evaluated through simulation using key metrics: (i) consortium network usage costs, (ii) computational fluctuations during hash processing and block delivery, (iii) seamless health data exchange and organization rates via Filecoin, and (iv) optimization of shared data and immutable ledger performance. Results demonstrate that the BHI-5 framework effectively meets the operational needs of 6G-ready edge healthcare environments, achieving secure, scalable, and optimized data management suitable for real-world healthcare applications. © 2017 IEEE.
Universiti Malaya, Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Kuala Lumpur, 50603, Malaysia; Bahria University, Bahria University, Karachi Campus, Department of Computer Science, Karachi, 75260, Pakistan; Universitas Brawijaya, Faculty of Computer Science, Department of Informatics Engineering, Malang, 65145, Indonesia; Marwadi University, Department of Computer Engineering-AI Big Data, Rajkot, 360006, India
