IGMM-Based Approach for Discovering Co-Located Mobile Users
IGMM-Based Approach for Discovering Co-Located Mobile Users
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DOI：
10.1109/glocom.2016.7841883
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Pedro M. Varela;Jihoon Hong;T. Ohtsuki
中科院分区：
文献类型：
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作者：
Pedro M. Varela;Jihoon Hong;T. Ohtsuki
如今，人们无论走到哪里都带着他们的移动的设备，作为社会人，他们整天与他人互动。因此，通过利用智能设备的这种大规模使用，它们提供了一种仅使用其捕获的环境无线电信号来共处一地的方式。在本文中，我们设计了一个协同定位系统，发现群体的人，在实时，高精度，通过利用他们测量的无线电信号的相似性。我们的方法是基于一个非参数贝叶斯（NPB）的方法称为无限高斯混合模型（IGMM），允许模型参数随观察到的输入数据而变化。该系统是以完全集中的方式设计的。因此，它使网络能够控制和管理所有用户组的形成。我们分析了我们的框架的性能，在聚类准确性方面，从现实世界的设置数据集，以证明其可行性。我们还比较了它的性能对社区检测为基础的聚类方法。在真实的数据集上的实验结果表明，该方法具有较好的准确性.
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.