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Title	Crowd Counting Via Wi-Fi Probe Requests: Integrating Feature Selection And Data Generation
ID_Doc	16660
Authors	Chaaben M.; Bouguila N.; Patterson Z.
Year	2025
Published	Proceedings - IEEE International Conference on Semantic Computing, ICSC
DOI	http://dx.doi.org/10.1109/ICSC64641.2025.00012
Abstract	Crowd monitoring is essential for smart city applications, particularly for optimizing public transit systems. To address this need, we propose a privacy-conscious crowd counting pipeline using Wi-Fi probe requests, designed to adapt to the challenges posed by MAC address randomization. Our approach leverages a random forest-based feature selection process to identify key Information Elements and frame attributes, and applies DBSCAN clustering with adaptive parameter optimization for device counting. To mitigate the limited availability of labeled data, a diffusion model generates synthetic tabular data, enhancing model robustness. Experimental results demonstrate improved accuracy in device counting, achieving a V-measure of 0.952, an average silhouette score of 0.789, and reliable clustering counts. © 2025 IEEE.
Author Keywords	Clustering; Crowd counting; Generative models; IEEE 802.11; Wi-Fi probe requests

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