AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion

TL;DR

AirTOWN is a privacy-preserving mobile app that offers real-time, pollution-aware POI suggestions by integrating AQI data, user preferences, and federated learning to promote health-conscious urban exploration.

Contribution

The paper introduces a novel privacy-preserving system combining federated learning and AQI data for real-time, pollution-aware POI recommendations in urban environments.

Findings

Effective integration of AQI data with personalized suggestions.

Successful implementation of federated learning for privacy protection.

Broad applicability through interpolation in sensor-sparse areas.

Abstract

This demo paper presents \airtown, a privacy-preserving mobile application that provides real-time, pollution-aware recommendations for points of interest (POIs) in urban environments. By combining real-time Air Quality Index (AQI) data with user preferences, the proposed system aims to help users make health-conscious decisions about the locations they visit. The application utilizes collaborative filtering for personalized suggestions, and federated learning for privacy protection, and integrates AQI data from sensor networks in cities such as Bari, Italy, and Cork, UK. In areas with sparse sensor coverage, interpolation techniques approximate AQI values, ensuring broad applicability. This system offers a poromsing, health-oriented POI recommendation solution that adapts dynamically to current urban air quality conditions while safeguarding user privacy.