## 3 Experiments
### Compression and Reconstruction  
For the CBN-VAE model, the CR is 24 (as shown in Figure 4, the input dimension is 120 and the output dimension is 5 with an identical data type of input and output). In the following experiments, the performance results of the model are tested with CR of 24. The learning rate is a hyperparameter that is usually set by the experimenter and can directly affect the final performance and convergence speed of the model. However, the difficulty of adjusting parameters is a big problem in neural networks. For example, for stochastic gradient descent (SGD) algorithm [37], at the beginning we hope that the parameter adjustment is bigger, the learning rate is larger, the convergence is accelerated. In the later stages of training the learning rate, we hope that the learning rate will be smaller, so that it can stably fall into a local optimal solution. At the same time, the learning rates required for various machine learning problems are not well set and require repeated debugging. For the optimal learning rate setting, a common way is to test the training loss value of the model at different values of learning rate. However, this method requires a lot of experimentation. The Adam optimization algorithm [38] is an adaptive parameter adjustment algorithm, which is an extension of the SGD algorithm. The Adam algorithm is different from the traditional SGD: SGD maintains a fixed value of the learning rate to update all parameters, and the learning rate does not change during the training process; Adam calculates independent adaptive learning rates for different parameters by calculating the first-order moment estimation and second-order moment estimation of the gradient. Adam algorithm has the advantages of fast calculation speed, suitable for non-stationary target, invariance of gradient diagonal scaling, etc. It is very suitable for solving parameter optimization problems with large-scale data. In the model training process, we use the Adam algorithm to automatically adjust the learning rate. First, we wanted to explore the best compression performance of the proposed model. For neural networks, increasing the number of training iterations can often improves network performance, so we tested the efficiency of different numbers of training iterations to the performance of the model. The experimental data was the training set and test set of Node 7. During the test, we averaged the summation of PRD value and SNR value of all mini-batches of the test set as the final result. The number of sensing data in a mini-batch was 120. The number of mini-batches of the training set of Node 7 was 289, and the number of mini-batches of the test set of Node 7 was 73. We report the average of PRD value and SNR value of the proposed model on the test set. The results are shown in Figure 5.  
At the beginning, increasing the number of training iterations can greatly improve the performance of the model. However, when the number of training iterations reaches 50 and above, the speed of the performance improvement of the model becomes slow. At this time, a large increase in the number of training iterations can only result in a small model performance improvement. In our experiments, when the number of training iterations reached 500, the proposed model achieved the best performance on the test set. The average of reconstruction error reached 0.0678 \({}^{\circ}\)C, the average PRD value was 2.3711%, and the average SNR value was 32.51 dB. When the number of training iterations exceeds 500, the performance of the proposed model on the test set begins to deteriorate, because the model over-fits the training data at this time, and the generalization ability of the model decreases, that is, the model has over-fitting. Increasing the number of training iterations will increase the computational consumption of the model. In practical applications, we believe that the optimal number of training iterations is 50.