Everyday mobility context classification using radio beacons

Inferring mobility states such as being stationary, walking, or driving is critical for transportation studies, urban planning, health monitoring and epidemiology. Our goal is to build a pervasive mobility classification system using smartphones while focusing on large deployment, which poses new design requirements: low processing complexity, high energy efficiency and high user-time coverage. Previous work focused on fine-grained location-based mobility inference using global positioning system (GPS) data. However, GPS-based mobility characterization raises many issues, such as spotty coverage and battery drainage, that makes it inadequate to meet our application goals. In this paper, we propose a new mobility classification method using radio beacons such as Global System for Mobile communications (GSM) and Wi-Fi traces. This method enables mobility-based applications to provide users with ubiquitous services while using energy-inexpensive existing infrastructures. We demonstrate how coarser-grained mobility states can be satisfactorily inferred from our method using a data set of five hours gathered from one user in five differently- characterized areas with 80.2% precision and 80.3% recall.