Guiding Wi-Fi Sensor Placement for Enhanced CSI-Based Sensing in Stationary Crowd Counting

Recent studies in Wi-Fi sensing have demonstrated the potential of channel state information (CSI) for indoor crowd counting. However, their practical application remains limited by constraints in sensing range and robustness. While multitransceiver setups have shown promise in enhancing performance, the impact of transceiver placement strategies on sensing effectiveness remains underexplored. In this work, we systematically investigate how sensor placement affects sensing performance in stationary crowd counting. We extend two foundational models, the Fresnel zone and sensing signal-to-noise ratio (SSNR) formulations, originally designed for single-target, single-link scenarios, and generalize them to characterize spatial sensing quality in multitarget, multilink environments. Based on this theoretical foundation, we propose a deployment evaluation model that quantifies sensing performance using a normalized metric termed the regional sensing quality (RSQ) and enables direct comparison among different transceiver topologies. Experimental results across diverse environments show that optimizing deployment can improve crowd counting accuracy by up to 20.48%, with our system achieving 98.14% accuracy for up to 20 individuals. This work provides the first framework that integrates theoretical modeling and practical validation to guide transceiver deployment for robust and scalable CSI-based stationary crowd sensing.