Secure and Efficient Offloading for Healthcare IoMT: Integrating Permissioned Blockchain and Deep Reinforcement Learning in MEC

Abstract The recent pandemic of COVID-19 has highlighted the critical importance of effective and secure Healthcare Internet of Medical Things (H-IoMT) technologies in preventing and controlling epidemics. Although many H-IoMT applications have been deployed, a significant number of them do not effectively tackle security and resource management concerns. This research introduces a new approach that combines permissioned blockchain with deep reinforcement learning (DRL) to improve security and resource allocation in H-IoMT systems. In order to tackle the security concern, the suggested H-IoMT system implements a permissioned blockchain approach to guarantee its security. In terms of resource allocation, we employ the mobile edge computing (MEC) approach to offload computing tasks to optimize the consumption of resources in the proposed system. The DRL approach is employed to enhance the efficiency of process offloading and processing over several edge servers, guaranteeing a high rate of transaction processing and effective utilization of resources. The simulation results demonstrate that the proposal not only enhances the total number of tasks handled but also ensures security by efficiently controlling blockchain throughput.