GLCR: Global-Local Complementary Representations for Wi-Fi Probe Request Association under MAC Randomization

Passive Wi-Fi sensing provides a promising way to support crowd monitoring by counting nearby devices when cameras are limited by occlusion or poor visibility. However, MAC address randomization breaks the connection between Wi-Fi probe requests and devices, which makes device counting unreliable. Existing methods use fingerprints derived from Information Elements (IEs) or rely on wireless measurements and frame metadata that can vary with deployment conditions. To jointly learn complementary representations of IE order and byte-level content without relying on such auxiliary information, we propose Global-Local Complementary Representation (GLCR), a self-supervised learning framework for Wi-Fi probe request association under MAC address randomization. GLCR learns representations from retained IE content and structure. Specifically, we construct a global IE sequence view that preserves IE order, and a local IE subsequence view that focuses on byte-level IE content. For the global view, we devise a hierarchical protocol encoder to capture dependencies at the byte and IE levels. For the local view, we perform salience fingerprint mining on the local subsequence to aggregate contextualized features using learned weights. We further adopt a context-gated refinement to control the local contribution to the global representation. Finally, we cluster the resulting embeddings without a predefined device count. Experiments on three public datasets and our WiDAR dataset show that GLCR outperforms all evaluated baselines, with relative gains of 3.25%–7.86% in clustering accuracy. The code is available at https://anonymous.4open.science/r/GLCR.