MODELING OF ENVIRONMENTAL DATA PROCESSING FOR MOBILE MONITORING SYSTEMS BASED ON UAVS AND THE IDW METHOD
DOI:
https://doi.org/10.28925/2663-4023.2025.30.955Keywords:
UAVAbstract
In the context of increasing technogenic risks and the need for rapid response to radiation-related emergencies, mobile environmental monitoring systems are becoming a critical component of national safety and public health protection. This article presents a comprehensive approach to modeling the processing of spatially distributed environmental data collected by unmanned aerial vehicles (UAVs) equipped with IoT-based radiation sensors. The proposed system architecture is based on a three-tier client-server model that separates data acquisition, processing, and visualization tasks. The UAV platform integrates lightweight, energy-efficient IoT sensors capable of real-time data transmission, including geolocation and radiation dose measurements.
The core contribution of this research lies in the development of a data processing algorithm tailored for dynamic, irregularly spaced measurements. The algorithm includes noise filtering, temporal and spatial aggregation, and spatial interpolation using the Inverse Distance Weighting (IDW) method. The IDW technique was selected for its simplicity, transparency, and high sensitivity to local variations, making it suitable for generating detailed heatmaps of radioactive contamination. The modeling process demonstrates the algorithm’s ability to transform raw sensor data into continuous contamination surfaces, enabling intuitive interpretation and decision-making.
Additionally, the article outlines the integration of the algorithm into a mobile application interface, which provides users with interactive access to processed data layers. The application retrieves pre-rendered heatmap tiles from the server based on the user’s viewport and zoom level, ensuring fast and responsive visualization. The system’s modular design allows for scalability and adaptation to various environmental monitoring scenarios.
The research highlights the importance of combining UAV mobility, IoT sensor intelligence, and spatial modeling to overcome limitations of traditional monitoring systems. It also identifies future directions for enhancing interpolation accuracy through machine learning methods and expanding the system’s applicability to other types of environmental hazards.
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