Читать книгу Design and Development of Efficient Energy Systems - Группа авторов - Страница 75

Further, control messages and acquired healthcare data are exchanged between the leaf device and edge data server. The end devices send the patient’s condition data collected from various sensors to the edge data server and gets instructions to perform from the backend server. The computation tasks are carried out by using various techniques like machine learning. The severs at the back end can compute any number of heavy data and uses intensive algorithms computation for processing all the data acquired from the end devices; the instructions are sent as a notification to the end device.

Data analytics are performed in real time and for achieving optimized solution with low latency edge computing is used. The computation can be done by the end IoT devices that are based on the instruction and guidelines from the edge device which is provided by the cloud server. The MQTT client runs on IoT end device while the MQTT server runs on the edge server that can also further request various services from the cloud. MQTT can also be replaced by CoAP IoT protocol as an alternative. Tensor Flow can be used for machine learning [1, 6, 32]. It is an open-source free library that consists of tools with a flexible ecosystem, community resources and libraries for building and deploying machine learning applications.