# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 1 Introduction  
Research has found sensor networks suitable to collect environment information, such as temperature, light and humidity in harder-to-reach areas. In recent years, with the development of information technology, wireless sensor networks (WSNs) have played an important role in a wide range of disciplines [1]. Unmanned vehicles need GPS and accelerometer to locate themselves, and to get information regarding their environments using a camera and lidar [2]. By fusing these multi-modal sensor data, they can also predict the moving trajectory of nearby objects. Similarly, environmental monitoring applications need temperature, humidity, wind direction, etc. The increase in WSNs applications and sensor nodes have led to an explosion in the amount of sensing data. If the sensing data collected by WSNs are directly sent to a gateway, it will not only consume a lot of power but also increase the probability of transmission errors. Because a sensor node's storage and portable energy resources is limited, it is important to design an energy-saving and redundant data compression scheme for WSNs. These factors have motivated efficient technologies to reduce energy consumption and extend the lifetime of WSNs. WSNs' data collection methods include direct transmission to the base station, multi-hop forwarding, data aggregation, and modeling with coding. Data aggregation is an important technologyfor data processing in WSNs. By aggregating the collected or received data, the amount of data to be transmitted can be effectively reduced [3]. In the existing data aggregation algorithm research, the node usually compares the relationship between its perceptual data and the surrounding node data. If the data is found to be close, it will not be submitted in order to reduce the transmission of redundant data. Although these aggregation algorithms reduce the amount of data and save the energy consumption of the node, they cause a loss of node data. In addition, when aggregating, the outlier detection is expensive and could introduce delays [4]. The Slepian-Wolf coding applies distributed source coding technology to perform non-collaborative data compression at the sources. However, it is not practical due to the lack of prior knowledge of the data correlation structure [5]. Model-based compression algorithms such as APCA [6] and PWLH [7] also have good compression ratio. However, they result in loss of sensing data, as they approximate data with temporal and spatial locality. There is research that shows that modeling with the encoding method is better for data transmission of WSNs [8]. It first models the original sensing data of the sensor node, following which the modeled weights are sent to the base station, which uses the weights to compress the original sensing data. Compressed sensing (CS) [9] provides a new data compression method for WSNs. The basic idea is that if the signal is sparse or compressible at a certain level, it can be reconstructed from a small number of linear measurements lower than the Shannon-Nyquist limit [10]. In recent years, the study of CS has led to many significant developments in the field of signal processing, including novel sub-Nyquist sampling strategies and a veritable explosion of work in sparse approximation and representation [11; 12]. In Ref. [13], the CS technology proposes a method to reduce the sensing data traffic for WSNs without the need for adapting to the data correlation structure. In Ref. [14], the CS principle was used as a compression and forwarding scheme to minimize the transmission data. At present, the CS requires a large amount of memory space to store the random sampling operator when the signal amplitude is large. Therefore, the classic CS is not directly applied to largescale application, and it will inevitably increase the computational complexity of encoding, so the CS compression is limited by the mobile embedded processor. At the same time, it is difficult to apply largescale WSNs while its energy is constrained.