Secured Fog Computing Based Framework for Intelligent Health Diagnosis Using Deep Learning and Internet of Things (IoT)

Abstract With the proliferation of IoT devices and cloud-based data processing technologies, cutting-edge smart, linked healthcare systems may be created. Smart healthcare systems examine patient data collected by the Internet of Things to improve treatment quality and lower healthcare expenditures. The amount of data produced by the billions of IoT devices connected to the Cloud-of-Things (CoT) is a significant problem for these systems. By bridging the gap between Internet of Things (IoT) gadgets and cloud computing, fog computing infrastructure provides a possible answer. It has the potential to provide low-latency, high-efficiency processing and storage for the Internet of Things at a tiny scale. However, privacy-preservation concerns are a major issue for healthcare systems due to the sensitive nature of patient data. In this study, we offer a unique secured computing architecture for intelligent healthcare diagnosis systems. For the sake of in-formation safety, a lightweight and trustworthy user authentication mechanism is made available. Electroencephalogram (EEG) data are used as input to an intelligent diagnosis system that use deep learning techniques to automate the identification of epileptic seizures. Security and performance evaluations, which compare the proposed technique to the current method for seizure prediction, show that it is secure and efficient.