论文标题
组中：从移动设备无线轨迹的组推理
Group-In: Group Inference from Wireless Traces of Mobile Devices
论文作者
论文摘要
本文提出了一个无线扫描系统，该系统可检测室内或室外环境中的静态人群或移动人群。 Group-IN仅从启用蓝牙的移动设备进行组推理的无线跟踪。这项工作中解决的关键问题不仅要检测静态组，还要检测具有基于多个方法的基于基于多个方法的组，只有无线无线扫描仪没有定位支持就可以观察到的无线扫描仪（RSSIS）。我们建议新的集中和分散的方案处理稀疏和嘈杂的无线数据，并利用基于图的聚类技术来从短期和长期方面进行组检测。小组IN提供了两个结果：1）在短时间间隔（例如两分钟和2）长期连接（例如一个月）中的小组检测。为了验证表现，我们进行了两项实验研究。一个由实验室环境中的27个受控方案组成。另一个是现实情况，我们将蓝牙扫描仪放置在办公室环境中，员工携带信标超过一个月。受控的和现实世界中的实验都在短时间间隔中导致高精度组检测，并根据jaccard索引和成对相似性系数对自由进行采样。
This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.
