Sniffing Only Control Packets: A Lightweight Client-Side WiFi Traffic Characterization Solution
Sniffing Only Control Packets: A Lightweight Client-Side WiFi Traffic Characterization Solution
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DOI：
10.1109/jiot.2020.3041671
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发表时间：
2021-04
影响因子：
10.6
通讯作者：
Lixing Song;A. Striegel;Alamin Mohammed
中科院分区：
文献类型：
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作者：
Lixing Song;A. Striegel;Alamin Mohammed
物联网（IoT）的发展为我们的日常生活带来了前所未有的便利。然而，随着连接到因特网的移动的设备数量的不断增加，无线网络环境变得比以往任何时候都更加拥挤。特别是，WiFi随着其在物联网中不断发展的作用，正在承担来自物联网和其他移动的设备的大量流量。因此，竞争设备的爆炸式增长、蜂窝技术的入侵以及内容丰富性的急剧增加，在WiFi上提供了比预期更多变的体验质量（QoE）。此外，这种变化往往发生在时间和空间上，这使得调试成为一个非常困难的问题。现有的主动方法往往昂贵或不切实际，而现有的被动方法往往过于狭窄。为了进行有效和非侵入性的WiFi流量表征，在这篇文章中，我们提出了一种新的被动客户端方法，通过利用帧聚合（FA）和块确认（BA）的属性，提供有效和准确的表征。设计的方法只需要捕获和分析某些类型的控制数据包，因此可以在计算能力有限的物联网设备上部署。在这篇文章中，我们表明，我们可以准确地得出重要的表征指标，如通话时间，排队信息和传输速率，只有少量的观察到的BA。我们通过大量的实验表明，我们的方法的有效性，并在密集的环境中进行验证研究的校园尾门。
The advancement of the Internet of Things (IoT) is bringing unprecedented convenience into our daily life. However, with the relentlessly increasing number of mobile devices connected to the Internet, the wireless network environment is becoming more crowded than ever before. Particularly, WiFi, with its evolving role in IoT, is shouldering a tremendous amount of traffic from IoT and other mobile devices. As a result, exploding numbers of competing devices, encroachment by cellular technology, and dramatic increases in content richness deliver a more variable Quality of Experience (QoE) on WiFi than desired. Moreover, such variance tends to occur both across time and space making it an extremely difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to be too narrow. To conduct efficient and nonobtrusive WiFi traffic characterization, in this article, we propose a novel passive client-side approach that delivers efficient and accurate characterization by taking advantage of the properties of frame aggregation (FA) and block acknowledgment (BA). The devised approach requires only capturing and analyzing certain types of control packets thus making it feasible to deploy on IoT devices that have limited computation power. We show in this article that we can accurately derive important characterization metrics, such as airtime, queuing information, and transmission rates with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.