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 placecentric 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 placecentric crowdsensing systems and applications.

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A user's location information is commonly used in diverse mobile services, yet providing the actual name or semantic meaning of a place is challenging. Previous works required manual user interventions for place naming, such as searching by additional keywords and/or selecting place in a list. We believe that applying mobile sensing techniques to this problem can greatly reduce user intervention. In this paper, we present an autonomous place naming system using opportunistic crowdsensing and knowledge from crowdsourcing. Our goal is to provide a place name from a person's perspective: that is, functional name (e.g., food place, shopping place), business name (e.g., Starbucks, Apple Store), or personal name (e.g., my home, my workplace). The main idea is to bridge the gap between crowdsensing data from smartphone users and location information in social network services. The proposed system automatically extracts a wide range of semantic features about the places from both crowdsensing data and social networks to model a place name. We then infer the place name by linking the crowdsensing data with knowledge in social networks. Extensive evaluations with real deployments show that the proposed system outperforms the related approaches and greatly reduces user intervention for place naming.