## 3 Experiments
### Compression and Reconstruction
When the number of training iterations is 50, the average PRD value of the model is 3.6915%, and the average of reconstruction error is 0.0973 \({}^{\circ}\)C. Although this result is not the optimal result for the proposed model, compared with 500 training iterations, the model performance hasnot decreased too much, and the training time and computation consumption have been reduced by 10 times. Since the proposed model needs to be calculated on the sensor node, we recommend setting the number of training iterations to 50 in practical applications. Figure 6 shows the reconstructed data and the original data of Node 7 for the proposed model, with the number of training iterations of 500. We reported 6000 sample points in the test set. The proposed model has high reconstruction accuracy, and the reconstructed data can closely approximate the trend and value of the original data. Although the model is trained to input data in the form of a mini-batch with 120 sample points, after the feature extraction by multiple convolution kernels of different sizes, the proposed model can still fit the approximate values of all the sample points separately, avoiding the reduction of the fitting performance of the model for a single sample point due to the large size of input data. The black line in Figure 6 shows the reconstruction error curve of the Node 7 test set samples. We find that the reconstruction error of the proposed model mainly comes from the parts of the original sample data, the value of which changes drastically. These cases are not well learned by the proposed model because of the low number and probability of occurrences in the original data sample points. We recorded the reconstruction results for all sample points in the Node 7 test set, and the number of sample points is 8520. For all sample points in the Node 7 test set, the maximum value of the proposed model reconstruction error was 1.2301 \({}^{\circ}\)C, the minimum value was less than 0.0001 \({}^{\circ}\)C, and the average value was 0.0678 \({}^{\circ}\)C. For these 8520 sample points, the reconstruction error value of the proposed model for most sample points was less than 0.1 \({}^{\circ}\)C, the number of samples with reconstruction error exceeding 1.0 \({}^{\circ}\)C was only 18, and the number of samples with reconstruction error exceeding 0.1 \({}^{\circ}\)C was only 868. In this experiment, we used IBRL to compare the performance of our model with other compression algorithms. The results are shown in Table 2. Since the stream data length of the CS algorithm cannot be too long, we selected 40,000 data points of Node 7 for performance testing of the CS algorithm. We divided this data into 8 segments with a segment length of 5000. We averaged the results of all segments as the final result of the CS algorithm and set the CR to 10. DPCM-o algorithm uses the Huffman's algorithm to generate a dictionary and then compresses the sensing data [16]. LTC algorithm generates a set of line segments which form a piecewise continuous function. This function approximates the original dataset in such a way that no original sample is farther than a fixed error \(\mathbf{e}\) from the closest line segment [16]. We set \(\mathbf{e}\) to 130. The experimental methods and results of the Stacked RBM-AE model are from the literature [30]. At the same time, we tested the Stacked RBM-VAE model by using the same network structure and test methods as the Stacked RBM-AE model. The variational part of the Stacked RBM-VAE model is similar to the CBN-VAE model. We referred to Reference [31] to design  
Figure 5: (**a**) The average PRD value under different number of training iterations, (**b**) The average SNR value under different training iterations. the CNN-AE model and set the network parameters and training details in the experiment to be the same as the CBN-VAE model. We also tested the compression performance of CBN-VAE at different compression ratios.