This demo paper introduces AirSense-R, a privacy-preserving mobile application that delivers real-time, pollution-aware recommendations for urban points of interest (POIs). By merging live air quality data from AirSENCE sensor networks in Bari (Italy) and Cork (Ireland) with user preferences, the system enables health-conscious decision-making. It employs collaborative filtering for personalization, federated learning for privacy, and a prediction engine to detect anomalies and interpolate sparse sensor data. The proposed solution adapts dynamically to urban air quality while safeguarding user privacy. The code and demonstration video are available at https://github.com/AirtownApp/Airtown-Application.git.
@article{arxiv.2502.09155,
title = {Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI Suggestion},
author = {Giuseppe Fasano and Yashar Deldjoo and Tommaso di Noia and Bianca Lau and Sina Adham-Khiabani and Eric Morris and Xia Liu and Ganga Chinna Rao Devarapu and Liam O'Faolain},
journal= {arXiv preprint arXiv:2502.09155},
year = {2025}
}