The world has seen a substantial growth in the number of senior citizens who often endure from heart disease in recent times and has seen a major spread of the viral disease (COVID-19) in recent months, resulting in thousands of deaths, particularly among the elderly and people suffering from heart disease. Among the most promising health care that seek to relieve the suffering of patients, particularly the elderly, by eliminating the trouble of going to hospital centers for treatment and enabling them to obtain medical attention in their homes. The IoT has been depended on and its use has seen broad adoption in health care, where it is commonly used remotely to track patient health. Fog computing expands the computational capabilities of the cloud to the edge devices of the IoT, enabling many smart devices in healthcare to provide services such as storing and retrieving information to their users. However in the traditional cloud-based system, there is a timing delay in providing a reliable and secure heart monitoring system. The main objective of this work is to develop a fog-enabled cloud-based (FECB) heart rate monitoring unit that allows better network use and energy consumption. In terms of performance metrics, such as latency and delay, the proposed system also outperforms the existing system.

Worldwide human health and economic has been affected due to the ongoing pandemic of corona virus (COVID-19). The major COVID-19 challenges are prevention, monitoring and FDA approved vaccines. IOT and cloud computing play vital role in epidemic prevention and blocking COVID-19 transmission. Mostly lungs and hearts are affected. Other than lungs many parts are affected which are not considered as prominent conversational cue. In this paper, we have proposed smart system that is effective through detection of pancreas, kidney and intestine. It detects acute pancreatitis, protein leak, microscopic blood leak, post infectious dysmotility and gastrointestinal bleeding. The data from the edge devices are collected and mapped into the cloud layer. The cloud consists of COVID-19 patients medical records which compare the user data with the existing patient records. Once the data matches it sends warning message to the user regarding the result of affected parts. Based on the result from KPI system, it analyzes with all data and using deep Convolutional Neural Network (CNN) it classifies whether the pancreas, kidney and intestine are affected or not due to COVID-19.