Automated iBeacon-Based Community Detection: A Data-Driven Approach

Jeongran Lee, Ahmet A. Akyamaç, Chitra Phadke, Hüseyin Uzunalioglu

Abstract: In this paper, we address the detection of face-to-face social interactions between people using unobtrusive, wearable iBeacon devices, as well as the determination of groups and communities that these individuals form. This is a challenging problem since this type of sensor data is very noisy, often incomplete with many missing values, and easily perturbed by motion and nearby obstacles, especially in very dynamic indoor environments. The key idea of our approach is to transform the original noisy sensor data into specific feature vectors by a series of statistical methods designed to overcome the challenges in the data. These features vectors, reflecting the interaction between people, are then clustered to reveal communities without the need to rely on localization information or pre-defined interaction models and parameters. Our data-driven approach provides robust community detection that is not sensitive to noise and missing signals, and automatically captures the dynamic interaction between people. Our approach is scalable for application in real-time.