Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI Suggestion

Giuseppe Fasano, Yashar Deldjoo, Tommaso Di Noia, Bianca Lau, Sina Adham-Khiabani, Eric Morris, Xia Liu, G. Chinna R. Devarapu, Liam O'Faolain

Abstract: 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.