## 1 Introduction
The more the number of algorithm coefficients, the better the performance, but the calculation energy consumption will become higher. The Lightweight Temporal Compression (LTC) algorithm proposed in Reference [15] is an efficient and simple lossy compression technique for the context of habitat monitoring. LTC introduces a small amount of error into each reading bounded by a control knob: the larger the bound on this error, the greater the saving by compression. In Reference [16], [PERSON] et al. proposed a purposely adapted version of the Differential Pulse Code Modulation (DPCM) scheme to compress the sensing data (in this paper, we call it DPCM-o), and it has better compression than LTC. In Reference [17], [PERSON] et al. proposed the compressed sensing (CS) method which provides a new direction for data compression in WSNs. The CS method can use the fewer measurements to recover large volumes of original data when the original data are sparse in a basis. CS can greatly reduce the cost of the system due to the use of sparse binary matrices [18]. CS requires that the signal be sparse or compressible at a certain level, otherwise the signal cannot be reconstructed. There are many signal recovery algorithms for fast reconstruction and reliable accuracy, such as the basis pursuit, the orthogonal matching pursuit (OMP), and the stagewise OMP (StOMP) [19]. BP has high computation complexity, and cannot be used for large-scale applications. OMP and StOMP adopt a bottom-up approach in signal recovery, and their complexity levels are much lower than that of BP. However, they require more measurements and have a lack of recovery guarantee. Machine learning (ML) is a technique for artificial intelligence, which has excellent mathematical fitting ability. In recent years, convolutional neural networks (CNNs) have demonstrated amazing abilities in various fields which has promoted the wide application of ML in various fields. Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM) are data generation models designed by ML [20; 21]. They use computational methods to improve model performance by detecting and describing consistency and patterns in training data [22]. CNNs can extract deeper and richer data hiding information through multiple layers of iterative convolution. Principal Component Analysis (PCA) algorithm is a dimensionality reduction technique from machine learning, which can be used to compress cluster data of cluster heads of WSNs [23]. In Reference [24], the authors described the use of PCA in WSNs employing supervised and unsupervised compression models. Reference [25] combined PCA compression with data aggregation to combine information from many sensor nodes. In Reference [26], the authors discuss the combination of ML methods and CS, which uses feed-forward deep neural network structures to aid CS signal reconstruction. In Reference [27], the authors propose a data compression algorithm that combines stacked autoencoder (SAE) with clustered routing protocol. In Reference [28], the authors use an unsupervised neural network scheme to analyze the data temporal or spatial correlation. Reference [29] developed an optimization technique using the back-propagation neural network to search for an optimal compression ratio to improve the quality of the signal and to improve the energy efficiency of the router node. Reference [30] studied the combination of RBM and autoencoder and proposed the Stacked RBM-AE compression scheme. The large deep convolutional networks have also been applied to data compression, but most of the research is currently limited to the field of image compression. For WSNs, most people use RBM or fully connected layers which only require a small amount of computation to compress sensing data, and few people study using convolutional networks to compress sensing data. An important reason for the lack of research is that the computational consumption of deep convolutional networks is large and it is difficult to apply to sensor nodes with limited computing power.