Passive RF Sensing for People Counting with Dense IoT Networks

Radio Frequency (RF) convergence, the joint use of radio signals for both communication and sensing, is gaining increasing attention in research and industry, mostly for its potential in Internet of Things (IoT) applications. By using RF signals of opportunity, such as those generate by the wireless networks, RF sensing exploits the effects of human bodies on the electromagnetic waves to detect both people presence and their movements without requiring wearable devices. In this paper, we address the problem of estimating the number of people in a monitored area covered by several RF nodes of a wireless network: to this aim, we propose a composite diffraction-based model, namely C-MAM, to capture the cumulative RF attenuation induced by multiple human bodies. Building on this model, we derive the analytical resolvability bounds that quantify the maximum number of distinguishable individuals based on the layout geometry and other physical characteristics of the wireless network. These bounds allow for the pre-deployment assessment of RF sensing performance under different scenarios. Theoretical findings are validated through simulations and experimental measurements, confirming the model's predictive accuracy and practical relevance.