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

TL;DR

This paper presents AirSense-R, a mobile app that uses real-time air quality data and user preferences to provide pollution-aware POI suggestions while preserving user privacy through federated learning.

Contribution

It introduces a privacy-preserving system combining sensor data, collaborative filtering, and federated learning for pollution-aware POI recommendations.

Findings

Effective real-time pollution-aware recommendations

Successful privacy preservation via federated learning

Adaptive system responding to urban air quality changes

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.