Wireless signal analytics in IoT systems can enable various promising wireless sensing applications such as localization, anomaly detection, and human activity recognition. As a matter of fact, there are significant correlations in terms of dimension, spatial and temporal aspects among wireless signals from multiple sensors. However, none of the wireless sensing research currently in use directly incorporates or exploits the signal correlations. Therefore, there is still substantial scope for improvement in regards to accuracy and robustness. We are introducing a novel framework called Signal Correlation Learning (SCL). This framework utilizes a directed graph to explicitly represent the signal correlation across various wireless sensors. We use signal embedding to depict the correlation features of a multi-dimensional sensor that arise from a multi-sensor system. Then, we perform Kullback-Leibler (KL) divergence on embedding vectors of any pair of sensors in the system to construct a subgraph at a given time point, which can measure the spatial signal correlation of sensors. Subsequently, several subgraphs spanning a specific time frame are fused into a coherent universal graph based on the small-world theory. This universal graph represents the three types of signal correlation simultaneously. A signal correlation aggregation structure is utilized to extract the features from the universal graph. These features can be used to address target sensing problems. We implement SCL in real RFID, Bluetooth, WIFI, and Zigbee systems, and evaluate its performance in three common wireless sensing problems including localization, anomaly detection, and human activity recognition. Extensive experiments demonstrate that our SCL framework significantly outperforms state-of-the-art wireless sensing algorithms by increasing $80\%\sim 190\%$ in terms of accuracy, and by increasing $160\%\sim 220\%$ in terms of robustness.

中文翻译：

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用于精确和鲁棒多模态传感的无线信号相关学习框架


物联网系统中的无线信号分析可以实现各种有前途的无线传感应用，例如定位、异常检测和人类活动识别。事实上，来自多个传感器的无线信号在维度、空间和时间方面存在显着的相关性。然而，目前使用的无线传感研究都没有直接结合或利用信号相关性。因此，在准确性和鲁棒性方面仍有很大的改进空间。我们正在引入一种称为信号相关学习（SCL）的新颖框架。该框架利用有向图来明确表示各种无线传感器之间的信号相关性。我们使用信号嵌入来描述多传感器系统产生的多维传感器的相关特征。然后，我们对系统中任意一对传感器的嵌入向量进行Kullback-Leibler（KL）散度，以在给定时间点构建子图，该子图可以测量传感器的空间信号相关性。随后，基于小世界理论，跨越特定时间范围的几个子图被融合成一个连贯的通用图。该通用图同时表示三种类型的信号相关性。利用信号相关聚合结构从通用图中提取特征。这些功能可用于解决目标传感问题。我们在真实的 RFID、蓝牙、WIFI 和 Zigbee 系统中实现 SCL，并评估其在定位、异常检测和人体活动识别等三个常见无线传感问题中的性能。 大量实验表明，我们的 SCL 框架通过增加$80\%\SIM 190\%$在准确性方面，并通过增加$160\%\SIM 220\%$在鲁棒性方面。