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SODAR: Nonobtrusive Off-Line Social Structure Reconstruction Through Passive Wireless Sensing

Understanding users’ social relationships plays an important role in many disciplines, including marketing, management science, and so on and is the fundamental context information required in many context-aware applications. However, despite significant research progress in social learning, sensing and reconstructing the off-line social structures in an accurate and nonobtrusive way is still a challenging open problem. In this paper, we propose SODAR, an off-line SOcial colocation Detection And network Reconstruction system, a social learning system that exploits wireless probes emitted by the smartphones carried by users to learn and infer their social relationships and reconstruct their off-line social structures. The probe capturing and filtering mechanisms collects high-quality wireless probe information, and the passive localization techniques are used to process the data and detect colocation events, which are used for the novel social representation learning. The learned social representation vector for each user contains rich social information and can be used to determine the social distances for each pair of users. With projection and clustering techniques, the off-line social structures can be visualized and reconstructed. We implemented the system and deployed the system to different indoor spaces covering more than 1000 m2. The evaluation results show that the SODAR system is able to reliably learn social representations for each user and effectively reconstruct the off-line social structures.