Detecting co-located mobile users

Co-location information of devices, people, and activities can be used in numerous applications in areas of social networking, mobile networking, spatial and socio-economics, and securing interactions. People co-location can be used to infer their communications and interactions. This information can be exploited for many purposes such as gaining understanding of human social interactions and behaviours. In this paper, we propose a real-time co-localization technique which provides accurate people co-location information with sub-meter accuracy. We construct a connectivity graph representing the potential colocated users based on pairwise similarity of RF measurements from user's mobile phones. We then apply community-detection tools to cluster users into co-located groups. Since our approach does not estimate the absolute location of individual users, it is robust to localization errors and protects the location privacy of mobile users. Our approach does not involve labour-intensive calibration as required for most localization approaches. We prototyped our proposed solution to detect co-located users in an enterprise building scenario. Android mobile users connected to our cloud localization server were accurately clustered according to their geographical proximity.