# IoT-Based Intelligent Monitoring System Applying RNN
## 4 Experiments  
Experiments were conducted to verify the functions of intelligent monitoring in the proposed system. Temperature data were gathered every 1 min. The six sensors were attached to various parts that included the temperature in the cryogenic freezer. Figure 9 shows the temperature data from the sensors. Sensor 1 is attached to the end of the condenser and Sensor 2 is located inside the freezer. Sensor 3 is placed on the high-stage compressor pipe, Sensor 4 is used for the outside temperature measurement, Sensor 5 is placed on the head of the low-stage compressor, and Sensor 6 is attached to the low-stage high-pressure part of the compressor. The temperature is measured in degrees Celsius. Figure 8: The temperature sensors located in the internal part of a cryogenic freezer. Figure 9: Temperature Sample data from six sensors attached to cryogenic freezer. Figure 10 shows the temperature data collected from a normally operating cryogenic freezer for 3 h. According to the location of the sensor, it can be observed that the temperature data exhibit a pattern over time within a certain range, as shown in Figure 10. From the data collected from each of the six sensors, 0.8 random data are extracted and used as a training dataset. The remaining data are used as a test dataset to evaluate prediction accuracy. We constructed both LSTM and GRU in order to implement applied RNN for predictive learning. The comparison results of LSTM and GRU (loss function graph and prediction results graph of each sensor) are shown in Figure 11. The graphs on the left side of Figure 11 show the loss function graph for the results of training with the LSTM and GRU methods using the data collected by each sensor. Overfitting did not occur until 20 epochs, and the loss value was less than 0.05, indicating convergence within 20 epochs. The graphs on the right side show the comparative analysis of the predicted temperature using the LSTM and GRU for the data collected from each sensor. Blue represents the training data up to 120 times, orange represents the actual data used for verification, and green represents the data predicted by inputting the verification data. This shows that there is little difference between the actual and predicted data after 120 iterations. The prediction results for each sensor's data show almost no difference from the actual data, which means that the built RNN prediction model is effective for intelligent monitoring and predictive maintenance. Experiments were performed on two models each applied to six types of sensor data. Experimental results confirmed that both models can be applied to predictive maintenance. The accuracy of the predictive model can be assessed based on how closely it falls within the threshold. However, the accuracy was within the range considering the same temperature within a certain threshold, and the accuracy value depended on the threshold value. Figure 12 shows a comparison of the accuracy of the LSTM and GRU results from the experiments. In the case of sensor 3, the threshold value is 5.0, which is larger than the others; therefore, the accuracy is lower than in other cases. In most cases, it can be noted that there is no significant difference between LSTM and GRU. However, GRU is known to be efficient in consuming computational resources. Figure 10: Comparison graph of temperature data from six sensors. Figure 11: _Cont._  
Figure 11: _Cont_.