MAC address randomization tolerant crowd monitoring system using Wi-Fi packets

TL;DR

This paper introduces two novel crowd monitoring algorithms, Vision and TrueSight, capable of accurately estimating device counts despite MAC address randomization in Wi-Fi packets.

Contribution

The paper presents Vision and TrueSight, innovative algorithms that enable MAC-address-based crowd monitoring to work effectively even with MAC address randomization.

Findings

Vision can group 440 random MAC addresses into one device.

TrueSight estimates device counts with over 75% accuracy.

Both algorithms operate without special software installation.

Abstract

Media access control (MAC) addresses inside Wi-Fi packets can be used for beneficial activities such as crowdedness estimation, marketing, and hazard maps. However, the MAC address randomization systems introduced around 2014 make all conventional MAC-address-based crowd monitoring systems count the same device more than once. Therefore, there is a need to create a new crowd monitoring system tolerant to MAC address randomization to estimate the number of devices accurately. In this paper, Vision and TrueSight, two new crowd monitoring algorithms that estimate the number of devices, are proposed to prove that MAC-address-based crowd monitoring is still possible. In addition to probe requests, Vision uses data packets and beacon packets to mitigate the influence of randomization. Moreover, TrueSight uses sequence numbers and hierarchical clustering to estimate the number of devices. The experimental results of this study show that even without installing any special software, Vision can gather 440 randomly generated MAC addresses into one group and count only once, and TrueSight can estimate the number of devices with an accuracy of more than 75% with an acceptable error range of 1.