An intelligent algorithm of amyloid plucks to timely fault-predicting and contending dependability in IoMT

Abstract

The security problem and the demand for service availability create risky conditions for individuals connected to the Internet of Medical Things (IoMT) technology. They are vulnerable to passive and active attacks involving the sharing of malicious information and data theft. Researchers have attempted to address these issues but often overlooked the significant impact of service priority and fault prediction on their methods for improving efficiency. The characteristics are undeniable when it comes to demanding appropriate service and rectifying the negative effects caused by faulty devices in an IoMT application. This study analyzes the challenges associated with IoMT and proposes a model and a three-phase algorithm called RPD for role mapping and pattern detection (RPD). RPD has successfully overcome the challenges posed by recent algorithms in defining service priority for various IoMT applications. It is capable of accurately predicting the area of faulty components and detecting and predicting them. The algorithm's features emphasize its role in providing a dependable service based on application priority, improving efficiency in service dependability, and reducing the cost overheads of defective components. Our approach involves transferring the responsibilities of IoMT application nodes onto the vertices of a subgraph while also defining their relationships using its edges. This study is inspired by the behaviors of human brain cells in facing Alzheimer's, Parkinson's, and acute Depression. It aims to develop a graph that can effectively identify patterns for predicting the distribution of amyloid plaques, specifically GPR56, α, and β proteins. To achieve this, we utilize the RPD algorithm, which identifies related patterns in technology applications based on their service priority predicted faulty components and halts them through phagocyte operations. This work demonstrates notable improvements in energy efficiency, communication delay, and dependability compared to recent Q-learning methods, with enhancements ranging from approximately 57.3% to 59.8%, 17.8% to 19.3%, and 37% to 41%, respectively.