Understanding the Coverage and Scalability of Place-centric CrowdSensing

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user loca-tions (such as stores, workplaces, and restaurants). Such sys-tems are transforming various consumer services (for exam-ple, local search) and data-driven organizations (city plan-ning). As the demand for these systems increases, our under-standing of how to design and deploy successful crowdsens-ing systems must improve. In this paper, we present a sys-tematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures ≈ 48,000 different place visits. Our analysis of this dataset examines is-sues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the character-ization of the collected data. Collectively, our findings pro-vide valuable insights to guide the building of future place-centric crowdsensing systems and applications