## 1 Introduction
In Reference [31], the authors used deep convolutional networks to compress electrocardiogram signals (ECG), but the network requires a lot of computation. In this paper, we explore how to apply CNNs to sensor data compression in WSN. The main problem we solve is how to reduce the computational consumption of the CNNs while ensuring that the performance of the network does not degrade. Reference [32] develops the Convolutional RBM (CRBM) structure to achieve object detection and obtained good detection results. CRBM respects the spatial structure of the image by sharing weights. We get inspiration from the design of CRBM and design an efficient convolution structure Downsampling-Convolutional RBM (D-CRBM). D-CRBM reduces network parameters by weight sharing and undirected graph characteristics of RBM, and uses downsampling to reduce the computational consumption of network. We combine D-CRBM with VAE and propose the CBN-VAE model. The model learns the data features through convolution calculation and reconstructs the data through the data generation ability of VAE and RBM. Our experiments demonstrate that the D-CRBM structure can significantly reduce the parameters and the computational consumption of the neural network without causing loss of network performance. Compared to traditional algorithms and neural network models, the proposed model has the best compression performance. We also test the transfer learning ability, fault detection ability and anti-noise ability of the proposed model. The energy analysis results prove that the computational efficiency of the model, and the model can be directly applied to the sensor nodes. We also propose a neuron pruning method to reduce redundancy in the network, which can further reduce the parameters and computational consumption of the model. The contributions of this paper are summarized as follow:  
* We developed a new neural network data compression model named CBN-VAE for WSNs. Compared with traditional neural networks, the proposed model has fewer parameters and computation consumption with negligible accuracy loss. We proved that the proposed model is more suitable for application on nodes in WSNs by experiment. * We proposed an efficient convolution structure named D-CRBM to reduce the amount of convolution operations, which can significantly reduce network parameters and computation consumption while preserving the performance of CNNs. * We proposed a new method of data compression, which has better reconstruction accuracy than the traditional algorithm under the same compression ratio (CR). * We proposed a new idea to judge the importance of neural network neurons. We used this idea to guide neural network pruning to further reduce network parameters and computational consumption. The remainder of this paper is organized as follows: Section 2 describes the CBN-VAE model architecture, the structure of D-CRBM and the training algorithm for the model. Section 3 shows the experimental results. At the same time, we give suggestions for the deployment of the CBN-VAE model. Specially, we introduce our neuron importance judgment ideas and the neuron pruning algorithms. In Section 4, we summarize our model. At the same time, we give the problems that we found in experiments and the further work we are going to do. The code of the CBN-VAE model is available online ([[https://github.com/LJianlin/CBN-VAE](https://github.com/LJianlin/CBN-VAE)]([https://github.com/LJianlin/CBN-VAE](https://github.com/LJianlin/CBN-VAE))).