RadonFAN: Intelligent Real-Time Radon Mitigation Through IoT, Rule-Based Logic, and AI Forecasting
Radon (Rn-222) is a major indoor air pollutant with significant health risks. This work presents RadonFAN, a low-cost IoT system deployed in two galleries at the Institute of Physical and Information Technologies (ITEFI-CSIC, Madrid), integrating distributed sensors, microcontrollers, cloud analytic...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/421498 |
| Acceso en línea: | http://hdl.handle.net/10261/421498 |
| Access Level: | acceso abierto |
| Palabra clave: | Radon monitoring Indoor air quality Low-cost sensors IoT Ventilation control Time series Deep learning |
| Sumario: | Radon (Rn-222) is a major indoor air pollutant with significant health risks. This work presents RadonFAN, a low-cost IoT system deployed in two galleries at the Institute of Physical and Information Technologies (ITEFI-CSIC, Madrid), integrating distributed sensors, microcontrollers, cloud analytics, and automated fan control to maintain radon concentrations below recommended limits. Initially, ventilation relied on a reactive, rule-based mechanism triggered when thresholds were exceeded. To improve preventive control, two end-to-end deep learning models based on regression-to-classification (R2C) and direct classification (DC) are developed. A quantitative analysis of predictive performance and computational efficiency is reported. While the R2C model is hindered by the inherent behavior of the time series, the DC model achieves high classification performance (recall > 0.975) with low computational cost (<4 million parameters, 7 million FLOPs). Modifications to the DC model are studied to identify potential performance bottlenecks and the most relevant components, showing that most limitations arise from feature richness and time series behavior. When evaluated against the existing rule-based ventilation system, the DC model reduces both unsafe radon exposure events and energy consumption, demonstrating its effectiveness for preventive radon mitigation. |
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