Big brother knows your friends: on privacy of social communities in pervasive networks
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
CrowdInside: automatic construction of indoor floorplans
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
Is there a place for serendipitous introductions?
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups. This information, known to group members but otherwise unavailable, can be leveraged in applications and protocols, such as recommender systems or delay-tolerant forwarding in ad hoc networks, to enhance the user experience. Group discovery using Bluetooth co-location is practical because:(i) Bluetooth is embedded in nearly every phone and has low battery consumption, (ii) the short wireless transmission range can lead to good group identification accuracy, and (iii) privacy-conscious users are more likely to share co-location data than absolute location data. This paper proposes the Group Discovery using Co-location traces (GDC) algorithm, which leverages user meeting frequency and duration to accurately detect groups. GDC is validated on one month of data collected from 141 smart phones carried by students on our campus. Users rated GDC’s groups 30% better than groups discovered using the K-Clique algorithm. Additionally, GDC lends itself more easily to a distributed implementation, which achieves similar results with the centralized version while improving user’s privacy.