## 1 Introduction  
Technologies concerning the convergence of medical equipment and information technology (IT) are consistently advancing in the era of the Fourth Industrial Revolution. Such convergent technology has been continuously evolving from e-health to u-health and further, to m-health, in a smart mobile environment in which there is a growing interest in \"smart healthcare\" and related research based on technological advancements [1, 2, 3]. E-health is a medical service that focuses on improving the delivery of health and medical information by connecting medical institutions and computer server systems using IT technology [4]. U-health utilizes IT technology in existing medical systems to provide health and medical information, knowledge, services, and products to consumers (individuals, medical institutions, and companies) [5]. In addition, it is a medical service that allows consumers to check their health status anytime or anywhere. M-Health is a medical service that utilizes mobile devices in medicine and public health [6]. Smart healthcare refers to a comprehensive medical service in which health-related services and IT converge to provide information, equipment, systems, and platforms concerning personal health and healthcare [7]. In recent years, the use of the Internet of things (IoT) sensors hasbeen applied in professional healthcare fields, including remote medical treatment, and in everyday life. IoT expands the development of medical systems for patient management and communication. The major advantages of using IoT in medical institutions include reduced costs, improved treatment outcomes, improved disease management, reduced errors, enhanced patient conditions, and improved drug management [8]. In particular, the necessity of non-face-to-face medical and healthcare services is increasing, owing to the outbreak of COVID-19 [9; 10]. IoT-based patient monitoring enables the real-time monitoring of biosignal data and patient management, regardless of time and space, through connections to hospitals and doctors; patients can also monitor their own health status and receive personalized health management services [11]. Accordingly, the demand for a remote monitoring system that enables continuous health management is growing as the healthcare industry advances and the general interest in health management rises [12]. Therefore, it is important for medical institutions to operate a biosignal monitoring service for examining patients' conditions and other to provide other services for promptly detecting abnormal disease symptoms, based on biosignal data. For performing biosignal monitoring in medical institutions, biosignals measured by IoT sensors must be monitored in real-time. However, real-time biosignal monitoring requires the collection, storage, and analyses of a considerable amount of biosignal data. Currently, monitoring systems are typically built on platform environments such as distributed systems or clouds. Such platforms are suitable for collecting and analyzing significant amounts of biosignals, but network traffic may occur during collecting, storing, analyzing, and monitoring biosignals in real time. The occurrence of network traffic may delay data collection or lead to a loss of the collected data. Therefore, edge computing technology has been employed for collecting and analyzing vast amounts of data [13; 14; 15]. Edge computing is a decentralized alternative to cloud computing technology, and it minimizes response time and network traffic by performing data collection, storage, and analysis in an edge node close to a terminal [16; 17; 18]. Edge computing is appropriate for real-time biosignal monitoring systems in medical institutions [19] because medical institutions require equipment for measuring biosignals in each ward. Thus, an edge node is configured in every ward to enable real-time biosignal data collection and emergency analysis.