Real-Time Organ Status Tracking System for Digital Healthcare

Artificial Intelligence (AI) and the Internet of Things (IoT) have greatly helped healthcare systems. Both these technologies allow for continuous tracking of patients’ vital signs and prompt treatment. In order to do this, this chapter suggests an IoT and edge computing–oriented healthcare scheme that is scalable, responsive, and dependable with minimal latency when providing patient care. The scheme entails the gathering of health-relevant data, its processing and analysis at edge nodes, as well as its long-term archiving and dissemination at edge data centers. Real-time resource allocation and patient scheduling are handled by the edge devices and edge supervisor. To evaluate system performance, simulations were run. End-to-end time, computation, optimization, and communication delay outcomes all show extremely positive signs. This research presents a taxonomy of current studies that use graph neural network (GNN)-based methods to optimize the control policies, including resource allocation, offloading tactics, routing optimization, and virtual network function orchestration. A neural network (NN) was utilized to simulate communication delay in order to assess system outcomes in a real-world scenario. For elderly or disabled people, as well as in pandemic conditions, the method is quite helpful.