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

Abstract

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.

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

“…In [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], the authors suggested local and global searching (simulated annealing and genetic algorithm)-enabled dynamic approaches to solve the offloading and and scheduling problem in IoT networks. The main goal was to reduce local and global search times for scheduling on heterogeneous fog and cloud nodes in the system, establish a secure environment among connected nodes, and minimize attack risk in the IoT network.…”

“…The goal was to execute workflow applications on different nodes in order to minimize the total delay of applications. Existing offloading schemes [ 1 , 4 , 7 ] only focused on coarse- and fine-grained applications. Therefore, there are no particular architectures and schemes for workflow applications.…”

“…Many IoT network-enabled algorithms were suggested for healthcare applications to protect data and store them cryptographically [ 6 ]. These static heuristics, such as heterogeneous earliest finish time, genetic algorithm, simulated annealing, and particle swarm optimization, are widely implemented to run healthcare applications in polynomial time [ 7 ]. Local and global searches (e.g., simulated annealing and tabu search) are fundamental guided candidate solutions to the aforementioned algorithms to achieve optimal results of healthcare applications in heterogeneous fog cloud networks [ 8 ].…”

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

“…Additionally, it interconnects medical devices with healthcare providers such as hospitals, private companies, or medical researchers [ 15 ]. IoMT combines IoT with conventional medical equipment and expands sensing and capabilities [ 16 ]. IoMT has the ability to transmit data across a network without demanding human-to-human or human-to-computer interaction [ 14 ].…”

Prediction of Intrinsically Disordered Proteins Based on Deep Neural Network-ResNet18

“…These methods promote the field of IDPs, yet ignore the overall structure of the protein, which leads to inaccurate prediction results. (ii) More recently, the use of machine learning has increased in the field of bioinformatics, especially to solve problems that are closely related to human life and health [15,16]. Methods to identify IDPs through machine learning, especially deep learning techniques, such as PONDR [17], DISOPRED2 [18], RONN [19], DISKNN [20], IDP-Seq2Seq [21], NetSurfP-2.0 [22], SPOT-Disorder2 [23], and RFPR-IDP [24], have also been developed.…”