Understanding the coverage and scalability of place-centric crowdsensing

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 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.

The night is young

“…In [6], the authors addressed the task of place categorization based on the automatic processing of opportunistically captured audio signals and still images. In this regard, our work is closely related to work by Chon et al [5], who carried out a two month deployment of a crowdsensing platform to collect 48,000 place visits from 85 participants in Seoul, to examine the coverage and scalability of place-focused crowdsensing.…”

“…As we explain later, the intentionality of our crowdsourcing task also allows to study issues related to the perception of social acceptability of video recording in everyday life. Finally, our study covers a much larger geographic area than [5,48,47], including two cities with linguistic and cultural differences, but also many areas around each city.…”

“…The engagement of mobile crowds to collect everyday life data using smartphones has followed two main directions in the literature. One approach relies on crowdsensing, i.e., the use of sensors in mobile devices to collect data (either at pre-defined regimes or opportunistically) without requiring human intervention [26,5]. While many sensor data types (location, accelerometer, WiFi) can be reasonably processed using data coming from smartphones in pockets or bags, other sensors like camera and microphones suffer from it.…”

An Efficient Participant’s Selection Algorithm for Crowdsensing

“…First, there are several systems and experimental studies on either experimental study on MCS coverage or general framework of participant recruitment [18], [19]. For example in [20] has performed a systematic study of the coverage and scaling properties of place-centric urban crowd sensing and shows promising results that MCS can provide relatively high coverage levels especially given area with large size. Then, there are also many theoretical studies on various task assignment and participant selection problems, playing tradeoffs among sensing cost, task coverage, energy efficiency [21], [22] and user privacy [23], and incentive.…”

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

“…In this application field as well as others (e.g., surveillance [11], crowdsensing through smartphones [12] and dynamic coverage [13]) there is a growing trend towards mobile sensing platforms. For air pollution monitoring in particular, innovative sensing strategies such as wearable air quality sensing nodes [14] and smart-phones used as mobile air quality sensors [15] are proposed.…”