## 2 Related Work
### Edge Node System Design  
Edge nodes are devices that can process and analyze data in real time near the data collection equipment. Using these edge nodes improves the real-time data processing capabilities and response times, while reducing network and server traffic overload. Therefore, it is a suitable method for collecting data from biosignal measurement equipment that generates large amounts of data. The edge nodes of the proposed system are network devices placed in each ward that perform server, gateway, router, and storage roles. Edge nodes collect and store the data transmitted by IoT pulse oximeters and further analyze and monitor emergency situations in real time using the stored data. In addition, they send messages and the collected biosignals to the cloud when emergency situations occur. The data collection technology collects biosignals measured by the IoT pulse oximeters. The IoT pulse oximeters transmit the data, including IR AC, IR DC, red AC, red DC, AMB, SpO2, and PR, through a wireless network. The transmitted data are collected and stored in edge nodes in real time. TCP/IP socket communication technology is used to stably and quickly collect five types of raw data and two types of analytical data measured by multiple IoT pulse oximeters. TCP/IP socket communication is appropriate for transmitting and collecting a large amount of data [37] owing to various advantages, such as revise errors, guaranteed flow control, and assured data transmission order. However, collecting multiple biosignals from one collection system can lead to network traffic in the long term, which might cause various problems, including decreased service speed and service access errors. Therefore, the loads on a server can be reduced by using edge nodes for stably collecting biosignals measured by multiple IoT pulse oximeters. The data transmitted through a wireless network consist of two packets, including a packet transmitting five types of raw data and another packet transmitting SpO2 and PR data. Table 1 presents the basic packet format. In Table 1, ID is the key for distinguishing IoT pulse oximeters, while the message command code (CDM) is an identifier for distinguishing between two packets. The cyclic redundancy check (CRC) determines whether or not there is an error in a packet. DATA represents the basic format for transmitting raw data and the data for transmitting analyzed SpO2 and PR. Data storage technology systematically stores the data measured by IoT pulse oximeters. The biosignals measured by IoT pulse oximeters are measured over time. These data must be processed by a technology capable of quickly and accurately processing a large amount of data acquired in real time. Thus, this study used InfluxDB to store biosignal data in real time, using a time-series database [38; 39]. Among the currently available time-series databases, InfluxDB is the most suitable for data storage because it is stable and can process data at a high speed. In this study, two tables are created for storing the raw and analyzed data in InfluxDB. Table 2 shows raw data, including measured time, device ID, IR AC, IR DC, red AC, and red DC. Table 3 shows the analyzed data, including measured time, device ID, PR, and SpO2. The emergency analysis and monitoring system analyzes emergencies using SpO2 and PR and sends the relevant data to the cloud, while showing the transmitted data in real time. An SpO2 between 81 and 90 is considered critical, while an SpO2 below 80 is considered an emergency. Results above 200 and below 50 are considered to indicate an emergency. When emergency situations occur, an emergency message is sent to the cloud in JavaScript object notation (JSON) format. In addition, real-time monitoring in the edge nodes is an important factor for medical institutions. The biosignal monitoring of patients in each ward is needed to observe patients' conditions in real time. The data measured by IoT pulse oximeters are visualized.