A Context-Aware IoT based Fraught Model for COVID-19 Patient Self-Monitoring

Muhammad Jamil, Shafiq Hussain, Muhammad Farhan, Muhammad Rehan Naeem


For human healthcare, the need for comprehensive systems for healthcare data sharing is ever-expanding. ContextAware Applications using the Internet of Things have inveigled each industry over the globe. IoT-supported healthcare systems have been developed with efficient gateways that react like a connection between cloud computing and multivarious sensors. This paper addresses the concept of monitoring Covid-19 patients with an IoT-based ContextAware System. The storage of healthcare systems' massive data on the cloud causes latency issues and creates a lot of trouble during real-time analysis. The introduction of edge computing for real-time analysis can reduce these issues. Our research proposed a fraught model that will monitor and track the patients’ health records daily for smart selftreatment. We introduced the concept of context-aware wearable sensors to minimize actuation, transmission, and processing. Our model entails a cluster of internet-enabled wireless sensors, an edge computing layer, a cloud computing layer for syncing edge computing layer data, and end-user layers. We anticipated the secure end-to-end authentication sub-layers in our proposed model for the security and privacy of patients' data.

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