跨域推断用于人体定位：将Wi-Fi RSSI数据应用于CSI训练模型
Cross-Domain Inference for Human Localization: Applying Wi-Fi RSSI Data to CSI-Trained Models
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中文总结 AI 辅助
本文研究利用RSSI数据跨域输入CSI训练模型进行人体定位，以约80%置信度预测位置，表明广泛物联网设备可用于Wi-Fi密集环境中的隐私侵犯。
中文摘要 AI 辅助
Wi-Fi信号数据可用于侵犯个人隐私。虽然许多现有方法依赖于信道状态信息（CSI），但在典型的物联网设备上收集这些数据通常需要提升操作系统权限和专门的驱动程序。因此，本文研究了利用接收信号强度指示（RSSI）数据预测人体位置的可行性。选择RSSI是因为即使在用户权限有限的设备上也可获取，因此更适用于更广泛的物联网设备。为了绕过获取训练数据以训练基于RSSI的模型这一繁琐过程，本研究使用了一个现有的Wi-Fi姿态预测项目。然而，该项目假设输入为CSI数据。因此，我们研究了跨域推断的可行性，即将RSSI数据输入到该现有的基于CSI的模型中。我们收集了一个RSSI数据集，并与一个人在房间内移动的视频地面真值同步，以评估模型的性能。该评估证实，在存在人体移动的情况下，RSSI数据可以以约80%的置信度预测位置。这表明，在CSI数据上训练的模型可用于评估由分贝毫瓦（dBm）值组成的低粒度RSSI数据，以大致定位采集空间中的人员。这些结果表明，在Wi-Fi密集的环境中，广泛的物联网设备可被用于隐私侵犯。
英文摘要
Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collecting this data on typical IoT devices often requires elevated operating system permissions and specialized drivers. Consequently, this paper investigates the feasibility of utilizing Received Signal Strength Indicator (RSSI) data to predict human locations. RSSI was selected because it is accessible even on devices with limited user permissions, and therefore is more applicable to a wider array of IoT devices. To bypass the tedious process of obtaining training data needed to train an RSSI-based model, an existing Wi-Fi pose prediction project was used in this research. However, that project assumed CSI data as input. Therefore, we investigate the feasibility of cross-domain inference, i.e., feeding RSSI data into that existing CSI-based model. We collected an RSSI dataset, synchronized with video ground-truth of a person moving within a room, to evaluate the model's performance. This evaluation confirmed that RSSI data can predict locations with approximately 80% confidence when human movement is present. This demonstrates that a model trained on CSI data can be used to evaluate low-granularity RSSI data consisting of decibel-milliwatt (dBm) values to roughly locate people in the collection space. These results imply that a wide range of IoT devices can be used for privacy invasion in Wi-Fi-dense environments.
发表机构
- University of Iceland（冰岛大学）
机构由 AI 辅助整理，请以论文原文为准。
