An Unsupervised Model for Detecting Passively Encountering Groups from WiFi Signals

In day to day life, people meet strangers while commuting in public transports, roaming around in a shopping mall, waiting at airport boarding areas etc., and thus form passively encountering groups. Detection and analysis of such groups are essential for providing services like targeted advertisements, supply chain management, information broadcasting and so on. However, identifying such groups is challenging because of the underlying dynamics, where an encounter between two subjects is entirely instantaneous without having a specific pattern. This problem has two steps - (a) identification of subjects in proximity and (b) detecting groups from the proximity information. In this paper, we develop an unsupervised model to identify subjects in proximity based on WiFi signal information and assign a proximity score to each pair of subjects based on a novel metric defining the degree of proximity. With the help of these concepts from network science, we then utilize a community detection mechanism to infer the passively encountering groups from the proximity score. The proposed model has been implemented and deployed over an academic institute campus. A study over 25 subjects for six months reveals that the proposed model can detect passively encountering groups with more than 90\% accuracy, even with heterogeneous devices under various real-life scenarios.