Smart Iot Based Secure Framework For Healthcare Monitoring System Using Dynamic Rule Soft Signaling Method

Abstract

Advancement in sensor technologies has resulted in the rapid evolution of the Internet of Things (IoT) applications for developing behavioral and physiological monitoring systems such as IoT-based student healthcare monitoring systems. Nowadays, a growing number of students living alone scattered over wide geographical areas, and tracking their health function status is necessary. In this work, an IoT-based healthcare monitoring model is proposed to continuously check vital signs and detect biological and behavioral changes via smart healthcare technologies. In this model, vital data are collected via IoT devices. Data analysis is carried out through the Dynamic Rule Soft Signaling (DRSS) method for detecting the probable risks of patient physiological and behavioral changes. The experimental results reveal that the proposed model meets the efficiency and proper accuracy for detecting the patient condition. This research work also mainly focuses on Electronic Health Records (EHRs) security requirements for storing, searching, accessing, sharing and auditing in Cloud Healthcare Monitoring System (CHMS) using Dynamic attribute-based encryption (DABE) system. After evaluating the proposed model, the Dynamic Rule Soft Signaling method and Dynamic attribute-based encryption (DABE) system have achieved the highest accuracy of 97%, which is a promising result for our purpose methods. The proposed methods’ results outperformed decision tree, random forest, and multilayer perceptron neural network algorithms.