Overhead-Free People Counting in mmWave Network Using IEEE 802.11bf Passive Sensing

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Abstract

Accurately assessing the number of people in a room is essential for enhancing operational efficiency, safety, and sustainability, enabling applications such as smart building management, energy conservation, and emergency evacuation planning. Wi-Fi sensing, favored for its privacy-preserving ca-pabilities and the ubiquity of Wi-Fi infrastructure, has become a popular method for such sensing tasks, including people counting. Within this context, our paper introduces a Convolutional Neural Network (CNN) model for millimeter wave (mmWave) Wi-Fi people counting, capitalizing on the IEEE 802.11bf amendment, and in particular its passive sensing framework. By evaluating Range-Doppler (RD), Azimuth-Doppler (AD), and Azimuth- Range (AR) map representations as inputs, we establish AR maps' superiority, achieving up to 98.57% accuracy for counting up to four individuals. Our solution, free of communication overhead, also minimizes energy consumption and computational requirements, offering a scalable and efficient sensing solution for people counting, particularly well-suited for Internet of Things (IoT) devices with limited resources.

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Networking

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