Real-time Surveillance System to detect and analyzers the Suspects of COVID-19 patients by using IoT under edge computing techniques (RS-SYS

The symptoms can be detected through the use of sensors in smartphones that produce a large amount of data that we use to detect symptoms of the disease, processing all data using sensors inside the mobile phone is almost impossible because the processing and storage capabilities are insufficient. For this reason, the data is sent to the cloud that takes Long processing time, high power consumption, delay in transmission and reception, which makes thinking about sending to the cloud useless, especially for data that needs fast processing. This means that a node is located near the end user's devices, which may be computers, smartphones, routers, etc. Its purpose is to reduce energy consumption, and data upload costs and reduce transmission and reception time. In our paper, we proposed a new architecture for a system Quick response to detecting suspected cases with the COVID-19 virus. There are two types of nodes; the first type is the sensor node, which can monitor the state of citizens such as temperature and colds, etc. The second type is the edge node, which may be computers, smartphones, routers, etc. It is intended to reduce power consumption and data download costs and reduce transmission and reception time. The application used the mobile crowdsensing by IoT protocols approach enabled by Real-time Surveillance and Response System to detect and analyze the Suspects of COVID-19 patients by using IoT under edge computing techniques (RS-SYS) proven effective for the response and diagnosis to patients in real-time. The proposed work algorithm was implemented by the Cup Carbon Simulator and the implementation of the application by MIT App Inventor compared to previous articles on the same topic.