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Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Enhancing Low-Cost Air Quality Sensors with AI for Smart Green Routing

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DOI: http://dx.doi.org/10.15439/2025F3919

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 369–374 ()

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Abstract. Urban air pollution poses significant health risks, especially in densely populated areas. Low-cost air quality sensors offer a scalable solution for high-resolution environmental monitoring, but their limited accuracy undermines their reliability. Moreover, navigation systems that aim to reduce human exposure to pollution require trustworthy, real-time AQ data to be effective. This paper presents the AIQS project (AI-enhanced air quality sensor for optimizing green routes), an ongoing initiative that combines artificial intelligence, sensor hardware optimization, and pedestrian routing innovation to address these challenges. AIQS applies machine learning techniques---including Multilayer Perceptrons and fuzzy logic---to correct sensor readings and integrates these improvements into the open-source MitH framework. In parallel, hardware-level optimizations, such as fluid dynamics simulations and pre-treatment modules, are explored to enhance sensor performance. The corrected AQ data is then incorporated into a configurable routing tool capable of estimating pollutant exposure and computing low-exposure pedestrian paths in urban environments. This paper reports on the methodologies adopted, initial implementation outcomes, and planned next steps, offering insights into the design of AI-driven, pollution-aware navigation systems for healthier and more sustainable cities.

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