An Intelligent Cloud Computing Context-Aware Model for Remote Monitoring COVID-19 Patients Using IoT Technology

This chapter proposes and investigates an intelligent context-aware model that adopts a hybrid architecture with both local and cloud-based components to monitor patients, particularly COVID-19 patients with mild symptoms while they are in their homes. The cloud-based part of the system makes storing and processing easier, especially that the data generated by ambient assisted living systems are huge, particularly with patients suffering from chronic diseases and require more frequent readings. On the other hand, the local part of the system monitors the patients in case of any sudden interruptions on the internet or any failure in the cloud system. The proposed model uses context-aware techniques by monitoring different physiological signals, surrounding ambient conditions, along with the patient activities simultaneously to build a better understanding of the health status of the COVID-19 patient in real-time, as the system will help doctors to detect if the patient has symptoms of the COVID-19. The results obtained experimentally prove how our proposed model can effectively monitor patients and detect emergencies accurately in imbalanced datasets through a case study on a patient with normotensive disorder.