## 1 Introduction
Changes in temperature, humidity, solar radiation, and soil pH were evaluated through simulation, and the execution time was 2.09 s. [PERSON] and [PERSON] (2019) proposed a fuzzy-PID controller with a wireless monitoring system to control the environmental conditions of greenhouses and to monitor their microclimate. The test demonstrated that the sensor nodes functioned accurately, collecting multiple greenhouse parameters simultaneously. The system can maintain optimal greenhouse conditions by regulating parameters such as temperature, humidity, and light intensity. [PERSON] et al. (2019) highlighted the use of a flexible control system to monitor the microclimate of the greenhouse using a neural network solution. Based on the outside temperature forecast, heating and cooling devices were controlled, and the inside temperature was adjusted and improved for crop production. A fuzzy logic-based control system was proposed by [PERSON] et al. (2019). Wireless data monitoring systems were used to monitor and control the internal environmental data such as temperature, humidity, CO\({}_{2}\), and illuminance. [PERSON] et al. (2019) observed that environmental variability within a greenhouse can be achieved and that it is economically feasible with the sensor fusion technique and the installation of multiple measuring points. [PERSON] et al. (2019) suggested a low-cost wireless sensor network to examine the horizontal temperature and humidity distribution inside a greenhouse. The static and instantaneous variation in average temperature and humidity were found to be 1.0-3.4 \({}^{\circ}\)C and 9-40%, and 5.6-9.0 \({}^{\circ}\)C and 11.8-42.3%, respectively. The number of sensors used during the experiment affected the accuracy of the measurement of temperature and humidity in a largergreenhouse. The data from the sensors were closely connected, and redundancy was also avoided. Therefore, data analysis within the different nodes should be handled carefully to improve accuracy and reliability under the requirements in order to comply with the application's demands [37]. Analyzing environmental microclimate conditions in CSGs is essential to evaluate and optimize crop growth conditions and to improve energy efficiency. Monitoring environmental parameters such as temperature, humidity, CO\({}_{2}\) levels, solar radiation, etc., helps maintain ideal growth conditions, leading to better yields. This information also enables the development of smart control systems for automated greenhouse management, optimizing resource use, and curbing energy consumption. Utilizing wireless sensor networks and integration technologies is essential for the precision control and management of greenhouse conditions. To maximize the monitoring and control of environmental factors in CSGs, the use of various sensors and their positioning depending on the degree of variability in environmental factors is required. The objective of this study was to investigate the spatial, vertical, and temporal variability of environmental variables in two CSGs during winter, offering insights for efficient environmental management through wireless sensor networks and sensor location.