An Adaptive Blockchain Framework for Federated IoMT with Reinforcement Learning-Based Consensus and Resource...

Area of Science:

• Computer Science
• Artificial Intelligence
• Cybersecurity

Background:

• The rapid expansion of the Internet of Medical Things (IoMT) generates massive sensitive healthcare data, posing challenges for traditional architectures in managing data streams with low latency, scalability, and security.
• Current IoMT systems face difficulties in real-time decision-making due to system overhead and delays, impacting critical healthcare operations.

Purpose of the Study:

• To propose a novel framework integrating machine learning (ML) with blockchain-based federated Internet of Things (IoT) clouds for efficient and secure healthcare data handling.
• To address the limitations of traditional IoMT architectures in managing large-scale, sensitive data streams.

Main Methods:

• Utilized Gradient Boosting Machines (GBM) for intelligent data storage optimization, Deep Q-Learning (DQN) for resource management, and Convolutional Autoencoders for enhanced privacy and security.
• Implemented a Long Short-Term Memory (LSTM) network for resource utilization prediction and an Adaptive Byzantine Fault Tolerance (ABFT) consensus protocol with Reinforcement Learning (RL) for improved transaction efficiency.
• Leveraged Hyperledger Fabric for a private blockchain network, ensuring a seamless flow of optimized data between framework layers.

Main Results:

• Achieved a 25% improvement in cache hit rates and a 30% reduction in read latency using GBM. DQN optimized resource management, reducing CPU load by 20%.
• Convolutional Autoencoders enhanced anomaly detection by 95%, with a 10% reduction in false positives. LSTM improved resource utilization prediction to 90%.
• The ABFT-RL consensus protocol demonstrated a 40% improvement in transaction throughput and a 20% reduction in transaction latency, outperforming PBFT and Raft by 43% and 31%, respectively.

Conclusions:

• The proposed ML-integrated blockchain framework offers a scalable, secure, and efficient solution for managing healthcare data in IoMT environments.
• The framework significantly enhances real-time decision-making capabilities, reduces system overhead, and improves overall performance in telemedicine and remote healthcare applications.