# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 1 Introduction
The analog CS technique is a novel strategy to sample and process sparse signals at a sub-Nyquist rate [15]. In Ref. [16], the problem of energy consumption for sensor nodes performing CS and DCS was addressed when both digital and analog CS were considered. The minimum energy CS-based data aggregation problem was studied to minimize the total energy consumption of a WSN in collecting sensing data from the whole network [17]. In Ref. [18], the CS based signal and data acquisition for WSNs was proposed, and a cluster-sparse reconstruction algorithm was proposed for in-network compression to achieve accurate signal recovery and energy efficiency. Although the segmented linear compression method can be used to reduce the dimension of high compression ratio by using polynomial approximation in the form of segmentation, it has poor smoothness, poor precision and abnormal change. In Refs. [19; 20], the data compression algorithms based on spatiotemporal correlation for WSNs were summarized. In Ref. [21], the run length coding method was proposed, which is suitable to compress the sensing data for WSNs. A lightweight data compression algorithm based on spatial correlation was proposed in ICACT [22], which saves more energy than wavelet compression in the case of the same distortion rate. However, it is mentioned above that the learning ability of the shallow learning algorithm is very weak for advanced features. The deep learning method can extract detection data in multi-level features, which makes the system have a strong data fitting ability by learning the deep features in the data. At present, there are few researches on how to use the deep learning model to compress the sensing data for WSNs. In Ref. [23], a method of using principal components analysis (PCA) to reduce the dimensionality of data was proposed. In Ref. [24], the unification of machine learning approaches with CS was also addressed, in which feed-forward deep neural network structures are used to aid CS signal reconstruction. In Ref. [25], a data compression algorithm based on the stacked Auto-Encoder (SAE) was proposed. This method combines the SAE and the cluster routing protocol. Compared with the traditional compression algorithm, the SAE can improve the accuracy of data fusion by 7.5%. In Ref. [26], a deep convolution network for ECG signal compression is proposed, which needs a large amount of calculation. The Restricted Boltzmann Machine (RBM) is a probabilistic model for a density over observed variables that uses a set of hidden variables (representing presence of features). In the standard RBM, all observed variables are related to all hidden variables by different parameters. It was widely used in classification and generation. In Ref. [27], the Convolutional RBM (C-RBM) was developed to achieve object detection. The C-RBM is a variant of the RBM model in which weights are shared to respect the spatial structure of images. In Ref. [28], a pre-training algorithm for the Deep Boltzmann Machines (DBMs) was described. In Ref. [29], a CS reconstruction algorithm was proposed, which amounts to two nested inference problems, one on the CS observation-matching problem, and the other on the RBM model. Compared with variational autoencoders (VAE) and generative adversarial networks (GAN), RBM has the simplest network structure and the minimum number of parameters. Correspondingly, RBM has a small computational energy consumption and is more suitable for use in WSNs with limited energy. In our survey, there is currently no research on how to directly use RBM to compress the sensing data for WSNs. Currently, we mainly explore how to use RBM to compress the sensing data. In our next research, we will also explore how to use VAE, GAN and other deep learning models to compress sensing data. In this work, we combined the RBM generation model with the nonlinear learning method of deep learning theory, and propose the Stacked RBM-AE model, which can compress and reconstruct the sensing data for WSNs.