# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 1 Introduction  
According to the Food and Agriculture Organization of the United Nations (FAO), the global population is expected to reach about 10 billion by 2050, which will require the steady growth of agricultural production to meet food needs [1]. A revolutionary approach to solving this problem is the establishment of plant factories, which are efficient but resource-intensive plant production systems that are able to regulate environmental parameters to ensure that crops achieve uninterrupted production throughout the year [2, 3]. Large greenhouse systems such as plant factories are complex time-varying systems. In order to ensure that plants grow in an optimal environment, environmental parameters must be monitored and managed in real time [1], which requires the support of wireless sensor networks. However, unstable wireless sensor networks can lead to packet loss and communication delays, which in turn reduce the overall measurement accuracy of the environmental monitoring system [4]. Therefore, it is important to design a reliable large-scale wireless sensor network to enhance the perception of the environmental status of plant factories and improve the authenticity of environmental data collection. In wireless sensor networks, link quality has a significant impact on data transmission. When data are transmitted over a poor-quality link, data transmission failures with high packet loss rates occur more easily, leading to excessive retransmission [5]. The accuracy of link quality estimation can have a significant impact on the efficiency of network protocols [6]. It is crucial to perform accurate link quality assessment in order to assist the upper-layer protocols in selecting better links for data transmission, thereby improving network efficiency [7]. However, traditional methods for link quality prediction have demonstrated limitations in achieving accurate results [8; 9]. In agricultural applications, such as plant factories and other large-scale greenhouses, wireless signals propagate through the channel with unique challenges. First, the signals experience gradual attenuation due to the increase in transmission distance. Additionally, multipath effects arise from phenomena such as reflection, scattering, absorption, and signal overlap. These effects occur when the signals pass through dense crops and agricultural facilities within the plant factory. Consequently, the communication link may become unreliable [10]. Therefore, when deploying wireless sensor networks, it is important to consider the impact of complex environmental factors on link quality. To address this problem, [PERSON] et al. [11] modeled the path loss based on exponential and polynomial functions in the scenario of dense crops planted in farmland, and implanted a particle swarm algorithm for the optimization with R\({}^{2}\) of 0.8307 in polynomial function fit and R\({}^{2}\) of 0.9392 in exponential function fit. [PERSON] et al. [12] examined the effect of the height of individual fruit trees on link path loss by placing transceivers on fruit trees at different heights and constructed a linear regression fitting model of path loss at the height of the transceiver, where the vegetation's effect is maximum. By using accurate and effective link quality prediction models to select high-quality links for communication, it not only ensures the reliability of data transmission [13; 14], but also assists routing protocols in selecting the next hop during data transmission, thus guaranteeing overall transmission efficiency [10]. In the field of environmental monitoring in plant factories, accurately reflecting the sensing status of the monitored area through a wireless sensor network is essential. Ensuring that the network nodes achieve maximum coverage of the monitored area is the core requirement for improving sensing accuracy [15]. However, the randomly deployed nodes exhibit a non-uniform distribution, which results in a reduced network coverage area.