The new generation healthcare monitoring system combines technologies of wireless body sensor network, cloud computing, and Bigdata, and there are still limitations in protocol security, response delay, and prediction of potential severity disease. In response to the above situation, an Internet Protocol Version 6 (IPv6)-based framework for fog-assisted healthcare monitoring is proposed. This framework is composite of body-sensing layer, fog layer, and cloud layer. The body-sensing layer generates physiological data, and fog computing node in fog layer collects and analyses time-sensitive data. Fog layer sends physiological data to cloud computing node in cloud layer for further processing. Mobile intelligent device connects fog computing node and helps individuals to predict the potential disease with its level of severity. The proposed framework uses advanced techniques such as IPv6-based network architecture, cloud–fog resource scheduling algorithm based on time threshold, and classification model of chronic diseases based on cascaded deep learning and so on. In order to determine the validity of the framework, health data were systematically generated from 45 patients for 30 days. Results depict that the proposed classification model of chronic diseases has high accuracy in determining the level of severity of potential disease. Moreover, response delay is much lower than Internet Protocol Version 4 (IPv4)-based cloud-assisted environment.
Battery Management in a Green Fog-Computing Node: a Reinforcement-Learning Approach
