## 2 Related Work
The edge computing-based real-time monitoring system proposed in this study involves installing an IoT pulse oximeter in every hospital ward and managing five types of raw data (i.e., IR AC, IR DC, red AC, red DC, and AMB) and two types of analysis data (i.e., SpO2 and pulses) measured from patients in real time. This system facilitates real-time patient management and prompt emergency response. Designing the Architecture of an Edge Computing-Based IoT Pulse Oximeter Data Collection, Analysis, and Monitoring System  
This study proposes an edge computing-based real-time patient management monitoring system using an IoT pulse oximeter. The proposed architecture is shown in Figure 1. This study designed and developed a system that monitors the health conditions and emergency situations of patients in each hospital ward by having them wear an IoT pulse oximeter. The proposed system continuously monitors patients' health conditions and provides information about their conditions by analyzing the collected health condition data. Furthermore, the proposed system provides nurses and doctors with the health information of patients using wireless communication and a cloud server. Doctors can analyze patients' health conditions from a long-term perspective based on the provided data. The system consists of three layers. Each layer has a defined task and provides services to the other layers. The sensing layer measures the biosignals of a patient in real time using an IoT pulse oximeter and sends the data to an edge node through a wireless network. Biosignal transmission stably and continuously transmits the measured data. This study thus proposes a TCP/IP socket-based transmission system because a TCP/IP socket does not cause data extinction, and the data are received in the order of their transmission. Therefore, it can stably transmit a large amount of data measured by multiple IoT pulse oximeters. Edge nodes collect, store, analyze, and monitor the data transmitted by IoT pulse oximeters. They also manage network connections to enable the reception of the data measured by the IoT pulse oximeters. The collected data are stored in a time-series database (TSDB) [34] in real time, and emergency situations are analyzed. Biosignals are time-series data that are continuously measured over time. Hence, such data are stored in a TSDB, which can store a large amount of time-series data in real time. The cloud converts biosignals saved in edge nodes into big data to be stored, analyzed, and monitored. Further, the cloud analyzes the state of edge nodes to manage IoT pulse oximeters. A NoSQL-based database is used to systematically store a vast amount of biosignals stored in edge nodes [35]. Various types of biosignals and patient information are required to manage the health conditions of patients. To manage diverse types of information, expandability and high flexibility are required, in addition to the capability for processing a large amount of data. Morevoer, the cloud consists of a real-time monitoring system to monitor all the wards and an alarm function that provides alerts when the patients at the edge nodes are in an emergency situation. Figure 1: Architecture of a multiple access-based real-time health management monitoring system.