## 3 Experiments
### Energy Analysis and Optimization  
The computational consumption required for model compression directly affects the application of the model and the lifetime of the sensor nodes. We want to minimize the computational consumption of the model while maintaining compression performance. We compare several different data compression neural network models from the number of network parameters, network computation complexity, training time, calculation time, etc. The experimental numerical results are shown in Table 4.  
Compared with other neural network models, the CBN-VAE model has the shortest compression time, the minimum number of floating-point operations (FLOPs) and the number of parameters. Using the CBN-VAE model to compress a sensing data sequence with the number is 120 needs 13,917 floating-point calculations (including multiplication and addition). At the same time, we test the single floating-point calculation speed of STM32F407 with Float Point Unit (FPU). When using the FPU, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**FLOPs**} & \multicolumn{2}{c}{**No. of**} & \multirow{2}{*}{**CP-Time (ms)**} & \multirow{2}{*}{**FP-Time (ms)**} & \multirow{2}{*}{**TR-Time (ms)**} \\  & & & & & \\ \hline Stacked RBM-AE & 35,850 & 19,032 & 0.2113 & 0.3873 & 1.0211 \\ Stacked RBM-VAE & 36,150 & 19,234 & 0.2817 & 0.4577 & 1.6549 \\ CNN-AE & 772,008 & 9245 & 0.9859 & 1.5493 & 2.6761 \\ CBN-VAE & 27,564 & 2415 & 0.1761 & 1.162 & 1.7606 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Neural network model specific details comparison. [FLOPs] denotes the floating-point operations of the model compression and reconstruction. [CP-time] denotes the compression time. [FP-time] denotes the compression and reconstruction time. [TR-time] denotes the training iteration time. These results are obtained when the number of mini-batches is 1, and the time results are the corresponding unit time. STM32F407 takes 0.072 us to execute a single floating-point calculation (multiplication and addition). In theory, using the FPU, STM32F407 will take 1.01 ms to complete the floating-point calculation of the model compression. For the sensing node, using STM32F407 as the microcontroller unit (MCU) may result in excessive power consumption of the device, so we also tested the single floating-point calculation speed of the STM32L4 with very low-power. Using the FPU, STM32L4 will take 2.53 ms to complete the floating-point calculation of the model compression. For the use of the CBN-VAE model in WSN, we propose two solutions. Because the reconstruction of the compressed data must be done by the server, we only discuss the implementation of the compression process. One solution is that the training process of the model is done at the server, and then the server sends the model parameters to the corresponding sensor node. The node uses these parameters to initialize the model and perform data compression. The disadvantage of this solution is that once the parameters of the model are trained, it is difficult to update afterwards because the parameter update requires retraining the model with an amount of new original data. The advantage is that the training process is completed by the server, and the computational consumption of the node can be greatly reduced. Another solution is that the training process of the model is completed by the sensor node itself, and then the node sends the model parameters to the server, and the server uses the parameter to reconstruct the compressed data. The advantage of this solution is that the model parameters can be updated at any time. The disadvantage is that the computational consumption of the training process is provided by the node. For neural networks, the model parameters are usually redundant. To further streamline the model, we use the neuron pruning method to further reduce the number of parameters of the model and the calculate consumption.