Solving the WiFi Sensing Dilemma in Reality Leveraging Conformal Prediction
Solving the WiFi Sensing Dilemma in Reality Leveraging Conformal Prediction
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DOI：
10.1145/3560905.3568529
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发表时间：
2022-11
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影响因子：
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通讯作者：
Kailong Wang;Cong Shi;Jerry Q. Cheng;Yan Wang;Min‐ge Xie;Yingying Chen
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作者：
Kailong Wang;Cong Shi;Jerry Q. Cheng;Yan Wang;Min‐ge Xie;Yingying Chen
随着智能环境和物联网设备的广泛部署，WiFi传感在支持广泛的应用方面展示了其极大的便利性和非接触式传感功能。然而，设计一个无处不在的WiFi传感系统的异构场景在实践中仍然是一个很大的困境，因为系统表现不佳时，测试数据是显着不同的训练数据所造成的域的变化。为了解决这个难题，现有的研究涉及额外的努力来开发新的功能，甚至在环境变化下重新训练原始模型。然而，没有一个能完全解决这一困境。在这项工作中，我们对域变化问题进行了全面的研究，以使WiFi传感在现实中具有鲁棒性和准确性。我们对域的定义是全面的，包括环境、周围设置、用户差异、用户面向方向、用户相对于WiFi传感器的位置以及用户参与时间范围。我们的创新是基于共形预测框架在所有领域实现可靠的WiFi传感。我们的方法量化了一致性（即，测试WiFi样本和训练样本之间的相似性），然后用最可能的类别标记测试样本。我们开发了一种新的跨域transformal预测方案的基础上的多元核密度估计，有效地评估和学习的一致性，每个域的训练数据。为了满足各种特定应用的要求，我们进一步开发了两种方法来融合来自训练域的一致性知识来执行预测。大量的实验与自我收集和公共数据集表明，我们的框架可以提高预测精度从30%到74%的改进，在三个最具代表性的基于WiFi的应用程序在六种类型的域变化。
With the wide deployment of smart environments and IoT devices, WiFi sensing has demonstrated its great convenience and contactless sensing capabilities in supporting a broad array of applications. However, designing a ubiquitous WiFi sensing system for heterogeneous scenarios in practice is still a big dilemma as the system performs poorly when the testing data is significantly different from the training data caused by domain variations. To address this dilemma, existing studies involve extra efforts to develop new features or even to retrain the original model under environmental variations. However, none of them can resolve the dilemma completely. In this work, we conduct a comprehensive study on the domain variation problem to make WiFi sensing robust and accurate in reality. Our definition of domains is comprehensive and includes environments, surrounding settings, user differences, user's facing directions, user's positions relative to WiFi sensors, and user participating time frames. Our innovation is to achieve reliable WiFi sensing across all the domains based on the conformal prediction framework. Our approach quantifies the conformity (i.e., similarity) between the testing WiFi samples and the training samples, then labels the testing samples with the most probable class(es). We develop a novel cross-domain transformal prediction scheme based on the multivariate kernel density estimation to effectively assess and learn the conformity of each domain in the training data. To meet various application-specific requirements, we further develop two approaches to fuse the knowledge of conformity derived from the training domains to perform predictions. Extensive experiments with both self-collected and public datasets show that our framework can improve prediction accuracies from 30% to 74% improvements in three most representative WiFi-based applications across six types of domain variations.