# IoT-Based Intelligent Monitoring System Applying RNN
## 3 Design of an IoT-Based Intelligent Monitoring System and RNN Model  
In the international standard operating guidelines for biobanks, the method of monitoring the internal temperature of cryogenic freezers has been applied as a way to minimize the possibility of damage to stored bioresources, but it depends on the various parts of cryogenic freezers. In addition, the following problem may occur in the method of monitoring only the internal temperature of the cryogenic freezer. The internal temperature of the reservoir remains unchanged for 8 h, even in cases of a sudden temperature rise of the storage facility, power shutdown, aging internal parts, over-load, and failure. Therefore, when the internal temperature of the reservoir actually changes, it causes immediate damage to bioresources. Hence, no biobank fully guarantees the maintenance of bioresources. We designed and implemented an IoT-based monitoring system for the cryogenic freezer to monitor the exact storage condition in real time before the temperature inside the storage changes. The proposed system can measure the front and rear temperatures of each component, such as the compressor, condenser, heat exchanger, and evaporator through which the refrigerant circulates, as well as the internal temperature of the cryogenic freezer. The temperature sensor data are sent to the cloud server and analyzed using an RNN-based predictive learning model so that the abnormal status of each part can be monitored in real time. Figure 4 shows the architecture of the IoT-based integrated monitoring system applying RNN for the predictive maintenance of a biobank. It was designed as client-server architecture, as shown in Figure 4. There are three main parts: embedded hardware, a cloud server, and a client, as shown in Figure 4. It consists of various components: sensors and main controller attached to a cryogenic freezer, sensor data manager, anomaly detector, RNN-based predictive learning model, maintenance manager in a cloud server, real time monitoring, and client. The IoT-based main controller, which is attached to the biobank system, transmits temperature data from the sensors to the cloud server over the Wi-Fi network. The cloud server consists of four component modules and databases. The maintenance manager connects and manages cryogenic freezers and clients. The sensor data manager receives the temperature data sent from each sensor, stores the sensing data in the database, and provides data to the RNN predictive learning model that runs the RNN learning and derives the next predicted value. The anomaly detector compares the  
Figure 3: The framework of IoT-based intelligent monitoring system. predicted value with the actual sensed value to determine the abnormal state of the biobank. According to the results of the anomaly detector module, the maintenance manager module notifies the biobank manager of the biobank. The biobank manager in the client part can monitor the biobank in real time through the web or app, and check the biobank status information and other information. The sensors are placed at various locations in the biobank, and the main controller is also attached. The RNN predictive learning model performs prediction learning with the stored temperature data, which are time series data, and compares them with actual measured temperature data values. The anomaly detector sends the status information to the maintenance manager when the difference between the predicted value and the actual value is greater than the threshold value for each sensor. Through the web/app UI/UX, the biobank manager can monitor the status of the biobank with smart devices in real time and respond immediately to abnormal conditions of the cryogenic freezer. An intelligent monitoring and integrated management system to support biobanks can provide an entire management process for biobank managers to guarantee biomass safety. The RNN models for the intelligent monitoring system are shown in Figures 5 and 6. During the RNN process, the information in the foreground data may be lost.