ABSTRACT
In today's world of medical science, remote patient monitoring devices are becoming more important and a future need particularly in the present COVID-19 situation as individuals are preferred to be kept isolated. Patients would be benefited from a suitable monitoring system that measures their important medical parameters such as pulse rate, oxygen saturation or SpO2, body temperature, blood pressure, and Galvanized Skin Response (GSR). This system can increase the medical staff efficiency by drastically decreasing their duties in hospitals and the need to attend to them individually. Patients in their home isolation may utilize the device as well, and their vital indicators may be checked by doctors remotely. In this work, we are prototyping a powerefficient, wearable medical kit and a resource-aware fog network set up to handle the Internet of Things (IoT) data traffic. The idea behind the design is to process the critical medical sensors' data in the fog nodes which are deployed at the edge of the network. The data thus received, is used for a machine learning-based solution for personal health anomalies and COVID-19 infection risk analysis. © 2022 IEEE.
ABSTRACT
With the incorporation of Internet of Things (IoT) in healthcare systems immense new possibilities have emerged in the modern healthcare services. In recent times where people around the globe are suffering from the Covid-19 pandemic, providing remote healthcare services maintaining necessary social distancing through e-Healthcare has become an urgent priority. e-Healthcare services like patient's real-time health monitoring, exchange of electronic health reports, tele-consultation, remote surgery etc. require reliable and timely delivery of data and responses. To provide for such critical requirements it is essential to prioritize the forwarding of different types of data varies based on their throughput requirements and the delay sensitivities. In this paper, we study the network Quality of Service (QoS) control mechanism for e-Health services using the Software-Defined Networking (SDN) approach in a Fog based Healthcare Environment. We distinguish the data generated from various types of devices into priority classes looking at their priorities, throughput and privacy requirements in the application. Hence, we propose, OpenHealthQ, an OpenFlow based traffic shaping model using OpenFlow Queues to handle the healthcare data. OpenHealthQ provides a secure, on-demand and low-cost access to Healthcare 4.0's most demanding computing infrastructure in a distributed cloud architecture with SDN based fog nodes at the network's edge. Experimental studies show that OpenHealthQ is capable of reducing response time of the end host and significant increase in network throughput compared to the Best-Effort (BE) approach.