Understanding the coverage and scalability of place-centric crowdsensing

TLDR

Crowd‑enabled place‑centric systems gather and reason over large mobile sensor datasets at everyday user locations, transforming consumer services such as local search and data‑driven city planning, and as demand grows, understanding how to design and deploy successful crowdsensing systems must improve. The study presents a systematic investigation of the coverage and scaling properties of place‑centric crowdsensing. We deployed a representative crowdsensing system for two months, collecting data from 85 participants on 48,000 place visits, and analyzed place‑temporal coverage, the relationship between user population and coverage, privacy concerns, and data characteristics. Our findings provide valuable insights to guide the building of future place‑centric crowdsensing systems and applications.

Abstract

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic 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 issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.