APPLICATION OF SET THEORY FOR THE MATHEMATICAL JUSTIFICATION OF DEVELOPING AN IOT SYSTEM FOR AUTOMATED SOIL MOISTURE MONITORING

Abstract

The article explores the application of set theory for the mathematical substantiation of an IoT-based system designed for automated soil moisture monitoring. The innovative approach integrates the concepts of the Internet of Things with modern automation methods to ensure efficient management of agricultural resources. The theoretical framework of the study focuses on developing a mathematical model that employs set theory principles to formalize the relationships between sensors, monitoring parameters, external conditions, and data processing algorithms. Special attention is given to the development of algorithms for collecting, analyzing, and processing data from soil moisture sensors, enabling continuous soil condition monitoring. These algorithms utilize set-theoretic operations to merge data from various sensors and filter out noise. Based on the proposed models, the article presents the architecture of an IoT system that includes hardware components such as sensors and controllers, network data transmission protocols, a cloud environment for data storage and analysis, and an interactive user interface. A detailed analysis of the practical aspects of system implementation is provided, covering sensor specifications, energy consumption, network stability, interoperability, and integration with external weather prediction modules. Particular emphasis is placed on data security during transmission and system resilience to failures. The expected outcomes of implementing the system include optimized irrigation processes, increased crop yields, reduced resource consumption, and minimized environmental impact. The system facilitates timely responses to changes in soil moisture levels, enabling informed resource management decisions. Additionally, the article examines the potential for scaling the IoT system to other agricultural applications, such as monitoring soil temperature, light levels, and acidity indicators. The findings validate the feasibility of using set theory to construct formal models that enhance monitoring accuracy, accelerate data analysis, and improve management efficiency in the agricultural sector.