使用WiFi信号推断人与人之间的接近度
Inferring Person-to-person Proximity Using WiFi Signals
摘要：
当今社会被不断增长的电信基础设施所包围。这种基础设施为感知和记录大量人类行为提供了重要的机会。人类移动模式是这种行为的一个突出例子，已经基于手机信号塔，蓝牙信标和WiFi网络作为位置代理进行了研究。然而，虽然流动性是人类行为的一个重要方面，但理解复杂的社会系统不仅需要研究个体的运动，还需要研究它们之间的相互作用。大规模感知社会互动是一项技术挑战，许多常用的方法（包括RFID徽章或蓝牙扫描）只能提供有限的可扩展性。在这里，我们表明，它是可能的，在一个可扩展的和强大的方式，准确地推断人与人之间的物理接近度从两个人携带的智能手机测量的WiFi接入点的列表。基于一年多来收集的大约800名参与者的纵向数据集，我们表明我们的模型比当前最先进的模型表现得更好。我们的结果证明了WiFi信号在社交感知中的价值以及它们对隐私的潜在威胁。
英文摘要：
Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches---including RFID badges or Bluetooth scanning---offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply.
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