Adaptive Resource Optimization for Blockchain Sharding in Medical Digital Twin Edge Networks: A Healthcare Data...

Area of Science:

• Blockchain Technology
• Healthcare Informatics
• Network Security

Background:

• Digital twin technology is crucial for virtualizing healthcare systems, but faces challenges in secure data sharing.
• Sensitive patient data and treatment protocols require robust privacy and security measures in edge networks.

Purpose of the Study:

• To present a novel Medical Digital Twin Blockchain Sharding (MDTBS) framework.
• To address security and privacy concerns in healthcare data sharing within digital twin edge networks.
• To maintain real-time responsiveness for clinical operations.

Main Methods:

• A dual-layer blockchain architecture: local chains (DAG consensus) for intra-hospital and global chains (DPoS consensus) for inter-hospital communication.
• An adaptive resource allocation model optimizing cluster head selection, consensus access, and resource allocation for maximum transaction throughput.
• A two-layer proximal policy optimization algorithm to manage dynamic medical environments and optimize resource allocation.

Main Results:

• The MDTBS framework demonstrated significant improvements in transaction throughput (15-25%, p<0.001) compared to baseline methods.
• Achieved sub-three-second emergency response times, crucial for clinical operations.
• Attained over 85% privacy preservation efficiency, enhancing data security.

Conclusions:

• The MDTBS framework effectively enhances security and privacy in medical digital twin edge networks.
• The adaptive resource allocation and optimization algorithm successfully manage dynamic healthcare environments.
• The proposed solution offers a scalable and efficient approach for secure healthcare data sharing.