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

Pedro M. Varela, Jihoon Hong, Tomoaki Ohtsuki, Xiaoqi Qin

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number7469848
Pages (from-to)308-319
Number of pages12
JournalIEEE Internet of Things Journal
Volume4
Issue number2
DOIs
Publication statusPublished - 2017 Apr 1

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Keywords

  • Clustering
  • co-location
  • Gaussian mixture model
  • mobile computing

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management

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