A Comprehensive Survey of Blockchain and Reinforcement Learning in IoMT

Abstract

The Internet of Medical Things (IoMT) integrates interconnected medical devices and sensors to enable continuous patient monitoring and real-time healthcare delivery. Despite its transformative potential, IoMT systems face critical challenges related to data privacy, interoperability, latency, and security vulnerabilities inherent in centralized cloud architectures. Blockchain technology, with its decentralized ledger, cryptographic integrity, and smart contracts, has emerged as a promising solution to secure sensitive medical data while ensuring transparency and compliance with regulations such as HIPAA and GDPR. Concurrently, reinforcement learning (RL) techniques, especially advanced deep RL algorithms, facilitate intelligent, adaptive task offloading in fog-cloud computing environments to optimize latency, energy consumption, and resource allocation. This survey synthesizes twenty recent studies addressing blockchain-enabled privacy-preserving frameworks and RL-based task offloading mechanisms in IoMT. It critically evaluates architectural designs, cryptographic innovations including zero-knowledge proofs and quantum-resistant signatures, and RL methodologies for dynamic resource management. Key research challenges identified include the lack of standardized interoperability protocols across heterogeneous IoMT devices and blockchain platforms, the computational overhead of quantum-resistant cryptography on resource-constrained devices, and the opaque nature of RL models hindering clinical trust. Future research directions emphasize developing unified communication standards, lightweight post-quantum cryptographic schemes tailored for IoMT edge devices, and explainable RL frameworks to foster clinical adoption. Ultimately, this comprehensive analysis delineates a pathway toward robust, scalable, and secure IoMT ecosystems capable of delivering efficient, privacy-preserving healthcare services in complex digital infrastructures.