IGMM-Based Approach for Discovering Co-Located Mobile Users

Nowadays people are carrying their mobile devices wherever they go, and as social beings they interact with others all day long. Thus, by exploiting this massive use of smart devices they provide a way to be co-located using only their captured environmental radio signals. In this paper, we design a co-location system that finds groups of people, in real-time, with high accuracy, by exploiting the similarity of their measured radio signals. Our method is based on a nonparametric Bayesian (NPB) method called infinite Gaussian mixture model (IGMM) that allows the model parameters to change with observed input data. This system is designed in a completely centralised manner. Hence, it enables the network to control and manage the formation of the all users' groups. We analyze the performance of our framework, in terms of clustering accuracy, with datasets from a real-world setting to demonstrate its feasibility. We also compare its performance against community detection based clustering method. Results on experiment with real datasets show a better accuracy favoring our approach against its counterpart.