# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 1 Introduction
Consequently, coverage holes and overlapping sensing regions are likely to occur in such case. These issues adversely affect the network's stability and quality of service, as noted in previous studies [16; 17]. In addition, the high density of nodes in some areas generates a large number of redundant messages, leading to unbalanced network loads, which in turn leads to channel conflicts and reduces transmission efficiency. In order to ensure the stability of wireless sensor networks and provide high-quality services [18], realizing effective coverage within the monitoring area has become one of the core problems to be solved [19]. Network coverage optimization aims to maximize the network coverage area while minimizing the number of nodes used. In recent years, the coverage problem of wireless sensor networks has attracted attention in research. Genetic algorithms (GAs) [20], particle swarm optimization (PSO) [21], the artificial bee colony algorithm (ABC) [22],and the simulated annealing algorithm (SA) [23] are the most popular methods applied. These optimization methods exhibit strong adaptability and are largely independent of the mathematical characteristics of the problem [24]. In addition to inaccurate link quality estimation and uneven node deployment, the network topology also greatly affects the efficiency of the routing protocols, leading to unreliable networks [25]. Based on the classification of network topology, wireless sensor networks can be categorized into single-layer network structures and multi-layer network structures. In a single-layer network design, there is only one sink node, and all the nodes directly send data to the common sink node through single-hop communication. Such design usually causes network congestion and overload in practical application, which leads to serious information loss problems and ultimately reduces throughput and transmission efficiency [26]. In addition, the single-hop communication method has limited network coverage, a characteristic that makes it ideal for building small networks but less suitable when dealing with large networks [26]. [PERSON] et al. [27] designed an IoT system with a single-layer network topology based on the ZigBee protocol, where each node uploaded data to only one gateway through single-hop communication for environmental monitoring such as temperature, humidity, and light in greenhouses. This system had limitations in large greenhouse scenarios due to its susceptibility to gateway failure, which could result in complete network breakdown. Additionally, the heavy traffic directed toward a single gateway increased the risk of congestion, further compromising the reliability and robustness of the network. Compared to single-layer networks, multi-layer networks have improvements in terms of network reliability and scalability. In multi-layer network design, the system is divided into clusters by using a cluster routing algorithm. Each cluster has a cluster head responsible for collecting data from other nodes within the cluster. In a large-scale network, data can be sent to the cluster heads through intermediate nodes, creating multiple communication paths. This redundancy significantly enhances fault tolerance, as the failure of a single node or cluster head does not result in the collapse of the entire network. Additionally, the hierarchical structure distributes network traffic more evenly, reducing the likelihood of congestion and ensuring stable and reliable communication in large-scale applications. [PERSON] et al. [28] designed a three-layer wireless sensor network for temperature acquisition and hierarchical data fusion for the problem of uneven temperature distribution in large greenhouses. [PERSON] et al. [29] designed a hybrid wireless sensor network based on the Wi-Fi and Lora protocols for long-range communication in large greenhouses.