IGMM-Based Co-Localization of Mobile Users with Ambient Radio Signals

Co-localization of mobile users combines methods of detecting nearby users and providing them interesting and useful services or information. By exploiting the massive use of smartphones, nearby users can be co-localized using only their captured ambient radio signals. In this paper, we propose a real-time co-localization system, in a centralized manner, that leverages co-located users with high accuracy. We exploit the similarity of radio frequency measurements from users' mobile terminal. We do not require any further information about them. Our co-localization system is based on a nonparametric Bayesian method called infinite Gaussian mixture model that allows the model parameters to change with observed input data. In addition, we propose a modified version of Gibbs sampling technique with an average similarity threshold to better fit user's group. We design our system in a completely centralized manner. Hence, it enables the network to control and manage the formation of the users' groups. We first evaluate the performance of our proposal numerically. Then, we carry out an extensive experiment to demonstrate the feasibility, and the efficiency of our approach with data sets from a real-world setting. Results on experiment favor our algorithm over the state-of-the-art community detection-based clustering method.