Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Prototyping and sampling experience to evaluate ubiquitous computing privacy in the real world
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Estimating Class Probabilities in Random Forests
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Internet social network communities: Risk taking, trust, and privacy concerns
Computers in Human Behavior
LiveCompare: grocery bargain hunting through participatory sensing
Proceedings of the 10th workshop on Mobile Computing Systems and Applications
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
Exploring Privacy Concerns about Personal Sensing
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
txteagle: Mobile Crowdsourcing
IDGD '09 Proceedings of the 3rd International Conference on Internationalization, Design and Global Development: Held as Part of HCI International 2009
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Ear-phone: an end-to-end participatory urban noise mapping system
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Mobile data offloading: how much can WiFi deliver?
Proceedings of the 6th International COnference
Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tapping into the Vibe of the city using VibN, a continuous sensing application for smartphones
Proceedings of 1st international symposium on From digital footprints to social and community intelligence
Accurate and privacy preserving cough sensing using a low-cost microphone
Proceedings of the 13th international conference on Ubiquitous computing
Snap and Translate Using Windows Phone
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Mobility prediction-based smartphone energy optimization for everyday location monitoring
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Medusa: a programming framework for crowd-sensing applications
Proceedings of the 10th international conference on Mobile systems, applications, and services
Ensemble of exemplar-SVMs for object detection and beyond
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Low cost crowd counting using audio tones
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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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.