## 3 Experiments
### Transfer Learning  
We believe that generalization performance is an important indicator for compression models. For the neural network model, the generalization performance of the network is also called the transfer learning ability. A common way to test the transfer learning ability of neural network is to test the trained neural networks by using different datasets. If a neural network trained with a single dataset can still achieve good results on other datasets, we believe that the neural network has a strong transfer learning ability. In this experiment, we first used the data of Node 7 to train the model and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **CR** & **PRD (\%)** & **SNR (dB)** & **Reconstruction Error (\({}^{\circ}\)C)** \\ \hline CS & 10 & 38.40 & 8.31 & 1.4143 \\ LTC & 13.93 & 8.45 & 21.46 & 0.2904 \\ DPCM-o & 13.85 & 8.00 & 21.94 & 0.2342 \\ Stacked RBM-AE & 10 & 10.04 & 19.97 & 0.2815 \\ Stacked RBM-VAE & 24 & 9.49 & 20.45 & 0.2926 \\ CNN-AE & 24 & 3.80 & 28.39 & 0.1075 \\ CBN-VAE & 24 & 2.37 & 32.51 & 0.0678 \\ CBN-VAE-b & 10 & 1.70 & 35.40 & 0.0454 \\ CBN-VAE-c & 40 & 2.52 & 31.98 & 0.0770 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Compression performance comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **CR** & **PRD (\%)** & **SNR (dB)** & **Reconstruction Error** \\ \hline Argo (temperature) & 24 & 1.18 & 38.56 & 0.0984 (\({}^{\circ}\)C) \\ ZebraNet (location/UTM format) & 24 & 7.23 & 22.82 & 114.22 \\ CRAWDAD (speed) & 24 & 2.02 & 33.89 & 1.0897 (km/h) \\ IBRL (voltage) & 24 & 6.61 & 23.58 & 0.0199 (V) \\ IBRL (humidity) & 24 & 4.04 & 27.85 & 0.5096 (\(\%\)RH) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance on different datasets. Figure 6: The reconstructed data and the original data of Node 7.  
get the model parameters of the model for Node 7. Then, we used the model parameters of Node 7 to initialize the model and test the compression and reconstruction performance of the model with Node 7 parameters for all nodes. At the same time, we trained each node separately to obtain the model parameters corresponding to each node. For all nodes, we set the number of training iterations of the model to 500, and we report the average PRD value, the average SNR value and the average reconstruction error of the model with different parameters on each node. These average values are for all sample data for each node's test set. Figures 7 and 8 show the average SNR values and the average reconstruction error of models with different model parameters for all nodes, separately. For Node 5 and Node 45 without sample data, we set the average SNR value and the reconstruction error to 0. The experimental results show that the proposed model has good compression performance for all nodes. The optimal compression performance of the model is obtained at Node 2, with the average SNR value of 38.98 dB and the average reconstruction error of 0.0387 \({}^{\circ}\)C. We can be seen from Figure 7, the average SNR value of the model is not significantly reduced by changing the model parameters. For all nodes, the maximum value of the digital difference in the average SNR value between the blue box and the '*' symbol in Figure 7 is 4.24 dB, and the minimum value is 0.01 dB. The average SNR values of the model which use the parameters of node self are both more than 30 dB. The high SNR value indicates that the proposed model can better recover useful information from the original data. In Figure 7, we use the upper and lower boundaries of the blue box to represent the confidence intervals of the average SNR values for the corresponding nodes. For most nodes, the average SNR values of the model which using the parameters of Node 7 are all within the confidence interval of the node.