## 2 Materials and Methods  
### Development of the Proposed VCMS  
Figure 2 shows a simplified framework for the proposed VCMS in the ICU. In short, it receives many real-time respiratory measurement signals from multiple ventilators and patient monitoring systems. Currently, respiratory signals are delivered to the server through  
Figure 1: Schematic illustration of proposed ventilator central monitoring system (VCMS) in the intensive care unit (ICU).  
the internet environment in the ICU. The responding server may become overloaded and crash if much information is received in real time. This poses a serious risk, especially for ventilators and patient monitors, as they require continuous real-time monitoring. Thus, the load-balancing web application is supported by reducing the traffic load to improve request efficiency, and HAProxy and Nginx can be applied to perform the round-robin algorithm [32].  
An application was developed to collect data and deliver control commands through TCP/IP socket connections for patient waves and numerical data from the ventilators and patient monitors through a load balancer. It was developed in Java (using the Apache MINA framework as a data collection server) to support operating system (OS) independence [33]. The collected data were saved in Kafka (or Apache Kafka) to make them available to consumers. It is a distributed data-streaming platform that provides unification, high throughput, and low latency for managing real-time data feeds. It exchanges the ventilator monitoring data (waveforms and numerical parameters) through a high-capacity and high-speed message queue [34]. ELK (ElasticSearch, Logstash, and Kibana) stacks were used to quickly analyze, store, and retrieve large amounts of data stored in Kafka. ELK provides meaningful insights into processing and analyzing large amounts of data in real time in the IoT field [35]. Logstash implements the collection, aggregation, parsing, and delivery of log or transaction data from various sources (e.g., DB and CSV files) to ElasticSearch. ElasticSearch retrieves and aggregates data from LogStash to obtain interesting information, and Kibana supports the visualization and monitoring of ElasticSearch using a quick search. However, ElasticSearch is limited to storing real-time patient respiratory data. To overcome this problem, MariaDB, a relational database for standardized data management, was used in the proposed VCMS [36]. The VCMS has the advantage of storing, quickly searching, and analyzing patient respiratory data, and it can monitor multiple patients in real time. As this is limited to processing through ELK and MariaDB, a remote dictionary server (Redis), an in-memory DB that processes data in memory using a key-value structure, was applied [37]. Data acquired in real-time were transmitted to a spring open-source application based on the Java enterprise from Redis [38], and real-time respiratory data were delivered to the central monitor and mobile applications through a load balancer in the web socket format in the ICU. Data encryption standard and advanced encryption standard (AES) encryption and transmission methods were applied to implement cybersecurity for the  
Figure 2: A simplified framework of the proposed VCMS in the ICU.  
transmitted data [39]. As needed, clinical staff can check longitudinal information (e.g., trend review, event review, and patient longitudinal information) by requesting the repository for the comprehensive diagnosis of individual patients in central monitoring and mobile applications.