Mobile‐fog‐cloud assisted deep reinforcement learning and blockchain‐enable IoMT system for healthcare workflows

Abstract

AbstractThe Internet of Medical Things (IoMT) is increasingly being used to secure blockchain technology to operate healthcare applications in a distributed network. The applications are mobile and can move from one place to another with different wireless connectivity. However, there are a lot of challenges that are investigated further. For instance, dynamic content values changed during mobile applications during any business goal. The workflow healthcare applications are complex as compared to coarse‐grained and fine‐grained workload in IoMT. In this article, the study analyzed offloading and scheduling problems for healthcare workflows in IoMT fog‐cloud network. Therefore, the study considered the problem as an offloading and scheduling problem formulated deep reinforcement learning as Markov problem. The study devises the novel deep reinforcement learning and blockchain‐enabled system, consisting of multi‐criteria offloading based on deep reinforcement learning policies and blockchain task scheduling with task sequencing and research matching methods for healthcare workloads in the IoMT system. The simulation results suggested strategies that reduced the communication and computation time for each application in the system.