# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 1 Introduction  
The unprecedented growth of the Internet of Things (IoT) has caused wireless sensor networks (WSNs) to receive considerable attention from the research community. WSNs consist of hundreds of limited-resource sensors connected via wireless links. The main purpose of WSN systems is to collect information about their surroundings. First, the sensor nodes gather data from the environment. Subsequently, they transfer these data to a base station (BS) or sink node for processing. Researchers worldwide have been interested in WSNs owing to their low cost and wide variety of applications. WSNs can be used in many terrains, such as land [1], underground [2], and underwater [3]. They also offer great promise for a variety of applications, such as military target tracking and surveillance [4; 5], natural disaster relief [6; 7], and biomedical health monitoring [8; 9]. It is difficult to monitor every sensor node because the majority of them are located in areas that are not easily accessible. Therefore, sensors may malfunction or be exposed to various factors. This leads to noise issues when the WSN operates, which significantly affects the system performance. One study [10] shows that the WSN performance is significantly degraded when receiving and transmitting data via wireless channels that are influenced by noise. The network may experience data loss or corruption. Many different noise-causing factors exist, and each has a different impact on the system, depending on the environment and the system itself. Therefore, studying the effects of noise is critical for increasing the effectiveness of the entire system. Various sources of noise can interfere with wireless networks. Generally, the noise is caused by internal or external events. Internal events are factors inside the WSN system that produce noise, and they occur when sensor nodes gather and measure information from their surroundings. In contrast, external events are noise-causing factors from the outside. These could include environmental changes, the sudden appearance of an obstruction between the sensor node and the base station, etc. External events mainly impact WSNs when transmitting data from the sensor nodes to the sink nodes. Although noise reduction in WSNs is not a new research topic, very few studies have specifically investigated noise caused by external events. This motivated us to carefully consider the noise caused by harsh weather conditions, including rain, snow, and fog. These weather conditions were modeled as attenuation models. The impact of these unfavorable weather conditions was considered when data were transferred from the sensor nodes to a sink node. This noise can negatively affect the reliability of the data because it causes some data packets to be lost when transferring from the sensor nodes to the base station. The missing data packets significantly affect the completeness of the data received by the sink node. Traditional methods to prevent missing data require the sensor to resend the packet data. However, this solution is not desirable because of energy loss, communication delay, and inefficiency. In recent years, missing data reconstruction has become the preferred approach. The process of missing data reconstruction involves the recovery or reconstruction of missing data through the utilization of previously collected data points [11]. Many studies use algorithms for data recovery. However, most of them do not efficiently use the data from readings from the past, present, and neighborhood because the proposed algorithms are relatively simple and ineffective. In [12], the authors proposed a data reconstruction algorithm that replaces the missing data with the average of the data series, relying on its own data history. This is quite straightforward. An approach based on machine learning was proposed to address this problem by exploring the correlation between data in the sensors [13].