What question did this study set out to answer?

The aim is to develop a framework that combines blockchain technology and machine learning to improve healthcare data management and security in IoMT.

February 13, 2026Scientific Reports0 citationsOpen Access

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

CMCH V. N. U. Bharathi MurthyVellore Institute of Technology University MSM. Lawanya ShriVellore Institute of Technology University

Key Points

The aim is to develop a framework that combines blockchain technology and machine learning to improve healthcare data management and security in IoMT.
Integration of reinforcement learning with adaptive byzantine fault tolerance for consensus.
Utilization of Gradient Boosting Machines for intelligent data storage optimization.
Implementation of Deep Q-Learning for resource management.
Employing Convolutional Autoencoders for enhanced privacy and anomaly detection.
Using Long Short-Term Memory networks for accurate resource utilization prediction.
Improved cache hit rates by 25% and reduced read latency by 30%.
Decreased maximum CPU load by 20% and improved management by 15%.
Enhanced anomaly detection by 95% and reduced false positives by 10%.
Achieved 40% improvement in transaction throughput and 20% reduction in transaction latency.
ABFT-RL consensus increased throughput by 43% and decreased end-to-end latency by 31%.

Abstract

The rapid growth of the Internet of Medical Things (IoMT) has increased the adoption of remote healthcare applications and telemedicine services. A Massive amount of sensitive healthcare care is being gathered daily by IoT devices. . Managing the continuous flow of data streams while maintaining low latency, scalability and security remains as a challenge in traditional IoMT architectures. These problems lead to delays in real-time healthcare decision-making which is critical and increases system overhead. . To address these problems, the proposed work introduces a novel framework that integrates machine learning with blockchain-based federated IoT clouds, customised for an efficient and secure platform for handling healthcare data. The framework includes Gradient Boosting Machines (GBM) for Intelligent data storage optimisation, which analyses historical access patterns and real-time data, improving cache hit rates by 25% and reducing read latency by 30%. The system performance is improved by Deep Q-Learning (DQN), ensuring resource management. Due to optimisation, the maximum CPU load is reduced by 20% and improved management by 15%. Convolutional Autoencoders are used to improve privacy and security. These helped improve anomaly detection by 95% and reduce false positives by 10%. Long Short-Term Memory (LSTM) network improves the rate ofresource utilisation prediction to 90%, and proactive resource management achieves a 25% reduction in latency spikes. The Adaptive Byzantine Fault Tolerance (ABFT) consensus protocol with Reinforcement Learning(RL), is integrated to improve transaction efficiency and dynamically adapts the consensus parameters. The proposed integration results in a 40% improvement in transaction throughput and a 20% reduction in transaction latency. In comparison of ABFT-RL consensus with PBFT and Raft consensus under similar workloads, the proposed ABFT-RL enhanced throughput by 43% and decreased end-to-end latency by 31%, offering improved scalability and responsiveness. A private blockchain network called Hyperledger Fabric is considered. In this proposed work, the optimised output of each layer is fed into the next layer, and this seamless flow of data gives an efficient architecture managing the complexities of the blockchain-based federated IoT cloud.

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