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

The 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. Nowadays, the usage of the Internet of Medical Things (IoMT) aware distributed healthcare applications has been increasing progressively. IoMT is an emerging evaluation in medical science that offers different sensors that are connected with human bodies and efficiently monitor their health. Whereas, all sensors in the IoMT system connected with different distributed hospital servers which can monitor the healthcare data in a better way. However, there is a big risk of security when many hospitals share their data. This study devises a novel distributed blockchain‐enable IoMT which monitors the critical situation of data of patients in the network as shown in the figure. The proposed system has the following unique characteristics. The findings of the work divided into three layers as shown in the figure. The system is friendly and easy to use for any patient. The proposed system is cost‐efficient where patients can easily use the distributed hospital service with the lightweight cost. The system monitors the 24/7 healthcare situation of any patients based on self‐driven deep learning methods which can make the decision based on complex parameters. All hospitals can share their data within any risk based on blockchain in the proposed syst