RateCount: Learning-Free Device Counting by Wi-Fi Probe Listening

Abstract

Counting Wi-Fi devices within access point (AP) coverage by listening to their probe request frames (PRFs) is a well-established research problem, fundamental to many Internet of Things (IoT) applications such as crowd management and public transportation scheduling. While commendable counting accuracy has been reported, existing approaches fall short in deployment convenience due to their reliance on machine learning, which necessitates 1) extensive data collection and training efforts for system setup, and 2) specialized model fine-tuning for operational maintenance. We propose RateCount, an accurate, lightweight, and learning-free counting approach to lower deployment costs. RateCount employs a provably unbiased closed-form expression to estimate the device count based on the rate at which APs receive PRFs, along with an error model to compute the estimation variance. We also demonstrate its application in people counting by incorporating a device-to-person calibration scheme. Through extensive real-world experiments conducted at multiple sites spanning a wide range of counts, we show that RateCount, without any deployment costs for machine learning, achieves comparable counting accuracy to the state-of-the-art (SOTA) learning-based device counting and improves previous people counting schemes by a large margin.