OpenFi: Open-Set WiFi Human Sensing via Virtual Embedding Confidence-Aware

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-14 DOI:10.1109/TNSE.2024.3496496

Han Liu;Liang Xi;Wei Wang;Fengbin Zhang;Zygmunt J. Haas

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引用次数: 0

Abstract

WiFi sensing technology utilizes Channel State Information (CSI) to analyze human behavior and plays a crucial role in mobile computing. WiFi sensing systems are typically deployed by collecting data in specific known environments, known as closed-set settings. However, in practical deployment, WiFi sensing systems may encounter unknown environments, generating unknown CSI patterns due to signal reflection, multipath effects, and interference. In such open-set conditions, the WiFi sensing system should possess the capability to recognize unknown CSI patterns, enhancing its security and reliability. In response, this work proposes an open-set WiFi human sensing method based on virtual embedding confidence-aware (OpenFi). The core of OpenFi is virtual embedding generation to simulate a realistic open-set feature space. This strategy minimizes both empirical and open-set risks, enabling OpenFi to recognize unknown CSI patterns effectively. We conducted extensive experiments on diverse datasets, covering various WiFi sensing tasks, including human identification, human activity recognition, and sign language recognition. Experimental results demonstrate that OpenFi accurately identifies previously unseen CSI patterns in open-set conditions, achieving significant improvements of up to 27% and 10.26% in the FPR95 and OSCR metrics, respectively.

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OpenFi：通过虚拟嵌入置信度感知的开放式WiFi人体传感

WiFi传感技术利用信道状态信息（Channel State Information， CSI）来分析人类行为，在移动计算中起着至关重要的作用。WiFi传感系统通常通过在特定的已知环境（即封闭设置）中收集数据来部署。然而，在实际部署中，WiFi传感系统可能会遇到未知的环境，由于信号反射、多径效应和干扰，产生未知的CSI模式。在这种开集条件下，WiFi传感系统应具备对未知CSI模式的识别能力，提高其安全性和可靠性。为此，本工作提出了一种基于虚拟嵌入置信度感知（OpenFi）的开集WiFi人体感知方法。OpenFi的核心是虚拟嵌入生成，以模拟真实的开集特征空间。该策略最大限度地降低了经验和开放集风险，使OpenFi能够有效地识别未知的CSI模式。我们在不同的数据集上进行了广泛的实验，涵盖了各种WiFi传感任务，包括人体识别、人体活动识别和手语识别。实验结果表明，OpenFi在开放条件下准确地识别了以前未见过的CSI模式，在FPR95和OSCR指标上分别实现了高达27%和10.26%的显著改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.