Wi-CCFAR: Wi-Fi CSI-Based Controllable False Alarm Rate Human Presence Detector With Normalizing Flow

In human presence detection for Internet of Things (IoT) applications, Wi-Fi channel state information (CSI)-based methods offer significant advantages in cost-effectiveness and privacy preservation. Existing approaches typically rely on feature engineering or deep learning techniques to process CSI data. However, these methods often struggle to accurately model the complex, probabilistic nature of CSI measurements, particularly in environments with nonhuman moving elements. This leads to suboptimal detection probabilities under low false alarm rate (FAR) conditions. Therefore, this article introduces Wi-CCFAR, a controllable FAR human presence detector utilizing Wi-Fi CSI data. First, we employ a normalizing flow network with spline transformations for data modeling, capturing the complexity of CSI measurement distribution. Second, to address the inherent limitation of the same input and output dimensions in normalizing flow networks, we introduce an efficient feature extractor, which reduces input dimensions and computational requirements while maintaining high detection probabilities by preserving essential information. Last, we implement an enhanced information bottleneck loss function, which balances data fitness and class divergence, to achieve favorable detection probabilities under low FAR conditions. Experimental results demonstrate that Wi-CCFAR outperforms conventional methods, achieving higher detection probabilities with lower false alarm probabilities, even in challenging scenarios with various interferences such as moving objects and multiple occupants.