# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 4 Conclusions  
The implementation of precise and effective environmental monitoring strategies is essential for understanding the dynamic variations of environmental factors within greenhouses. In this study, a wireless sensor network system strategy was proposed for improving the reliability of wireless sensor networks for environmental monitoring in plant factories. Firstly, this study proposed a hybrid wireless sensor network architecture based on multi-hop clustering. Building on this architecture, a link quality prediction model tailored for plant factory scenarios was developed and evaluated, achieving high prediction accuracy with an R\({}^{2}\) of 0.9962. Additionally, it was observed that the influence of agricultural facilities and vegetation on link quality was most significant when the node height was 0.7 m. Finally, based on the output of the link quality prediction model, a reliable network coverage optimization model and a clustered routing protocol were designed and evaluated. The evaluation results demonstrated that under optimal deployment conditions, the required number of nodes was 33, achieving a CR of 97.512%. The approach proposed in this study offers several key advantages, providing a robust and scalable solution for ensuring reliable communication in wireless sensor networks deployed within plant factories. By utilizing a hybrid WSN architecture that combines Wi-Fi and ZigBee communication protocols, this strategy effectively mitigates the challenges posed by varying link quality, which is a common issue in such environments. The nonlinear link quality prediction model developed in this study significantly enhances the accuracy of environmental monitoring by considering the interference and signal attenuation caused by dense crops and agricultural structures, which are typical in plant factory scenarios. Moreover, the integration of a particle swarm optimization (PSO) algorithm for  
Figure 9: Cluster routing protocol. network coverage optimization, coupled with a reliable cluster routing protocol based on the K-means algorithm, contributes to improved data transmission efficiency and network load balancing. Looking ahead, this strategy holds considerable potential for adaptation and extension to other agricultural applications where wireless communication is essential. For instance, large-scale greenhouses, agricultural farms, and smart city environments can benefit from the proposed approach, with necessary adjustments to communication protocols and routing algorithms to suit specific environmental conditions. Additionally, although the proposed approach offers a more reliable strategy for wireless sensor network systems, the link quality prediction model can be further improved to adapt to environmental factors such as climate variations and interference from other wireless devices, thereby enhancing its applicability and performance in dynamic environments. Overall, this research provides valuable insights and a solid foundation for the development of reliable large-scale wireless sensor networks in plant factory environments. By improving network reliability, the strategy supports the stable operation of environmental monitoring systems, ultimately contributing to the optimization of plant growth conditions. The methodology proposed in this study can serve as a basis for future advancements in wireless sensor networks, paving the way for their deployment in agricultural and environmental monitoring applications at a larger scale. Methodology, Y.Z. and J.Z. ; Software, Y.Z. and X.Z. ; Validation, W.L. and Y.Z. ; Formal analysis, W.L. ; Investigation, W.L., Y.Z., X.Z. and Q.W. ; Resources, X.Z. and W.C.; Data curation, W.L. ; Writing--original draft, W.L. ; Writing--review & editing, L.Z. and W.C.; Visualization, L.Z. ; Supervision, J.Z. ; Project administration, Q.W. and J.Z. ; Funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (Project No.