Tracking Pandemics: A MEC-Enabled IoT Ecosystem with Learning Capability

摘要

The COVID-19 pandemic has resulted in unprecedented challenges to global society and the healthcare system in particular. The main objective of this article is to introduce an end-to-end Internet of Things (IoT) ecosystem for healthcare that uses an open source hardware and interoperable IoT standard for eHealth monitoring in general, and COVID-19 symptoms (e.g., fever, coughing, and fatigue) in particular. The system is designed to monitor the physical conditions of human subjects and send the data to a hierarchical multi-access edge computing (MEC) framework. Such a system is expected to be cognizant, taskable (i.e., tasks can be assigned to any computing process in the system), and adaptable. To this end, we demonstrate how a learning method can be introduced in the ecosystem to achieve taskability and efficiency. Specifically, the proposed system utilizes a shared representation learning process to extract actionable information from large volumes of high-dimensional data obtained from IoT edge devices. These edge devices are enabled with tri-sensors for real-time monitoring of COVID-19 symptoms. The feasibility of the proposed system is evaluated by testing real datasets.