Towards Exploiting Wi-Fi Signals From Low Density Infrastructure for Crowd Estimation

Tonetto, Leonardo; Untersperger, Moritz; Ott, Jörg

doi:10.1145/3349625.3355439

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…1) Wi-Fi Management Frames: Mainly due to its simplicity and reported accuracy [2], [19], crowd monitoring using Wi-Fi management frames is extensively used. In principle, all Wi-Fi enabled devices send a series of management frames, often used to search for available access points and to establish/maintain existing connections.…”

Section: B Crowd Flow -Using Multiple Tagsmentioning

confidence: 99%

Where Is My Tag? Unveiling Alternative Uses of the Apple FindMy Service

Tonetto

Carrara

Ding

et al. 2022

2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Bluetooth trackers, or tags, have quickly become ubiquitous and widely supported by multiple vendors. Beyond their original design of finding lost objects, these devices have the ability to extend the capabilities of current wireless smart devices. Since its launch in 2019, Apple's FindMy enables any devices from their brand to be easily tracked by more than 1 billion active iPhones and iPads on the market. While convenient, these systems may even serve further uses, including as a result of this work, crowd sensing and a side channel for mobile communication. But they also raise privacy concerns for their users. In this paper, we demonstrate how Apple FindMy can be used as a privacy-friendly tool for crowd monitoring, and how it may inadvertently leak information on a person's location in case of deliberate tracking. Additionally, we design and evaluate a proof of concept protocol, using the Apple FindMy and a crafted tag using a simple microcontroller. We show how such system could be used to transmit information at very low bit rates, while the devices transporting the information remain unaware of this covert channel, yielding an out of band communication channel.

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Section: B Crowd Flow -Using Multiple Tagsmentioning

confidence: 99%

Where Is My Tag? Unveiling Alternative Uses of the Apple FindMy Service

Tonetto

Carrara

Ding

et al. 2022

2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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“…We use a wireless sniffing kit inspired by [18]. The kit consists of a battery-powered Raspberry Pi 3 (Model B) and four external Wi-Fi adapters (Fig.…”

Section: Data Collectionmentioning

confidence: 99%

“…Our work differs from these in that we utilize timing information of the probe requests captures. Other approaches demonstrate how the radio signal characteristics can be exploited for similar crowd measurements, namely by analyzing Received Signal Strength (RSS) [4,18] and Channel State Information (CSI) [23].…”

Section: Related Workmentioning

confidence: 99%

An Online Method for Estimating the Wireless Device Count via Privacy-Preserving Wi-Fi Fingerprinting

Torkamandi

Karkkainen

Ott

2021

Lecture Notes in Computer Science

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Initially envisioned to accelerate association of mobile devices in wireless networks, broadcasting of Wi-Fi probe requests has opened avenues for researchers and network practitioners to exploit information sent out in this type of frames for observing devices' digital footprints and for their tracking. One of the applications for this is crowd estimation. Noticing the privacy risks that this default mode of operation poses, device vendors have introduced MAC address randomization-a privacy preserving technique by which mobile devices periodically generate random hardware addresses contained in probe requests. In this paper, we propose a method for estimating the number of wireless devices in the environment by means of analyzing Wi-Fi probe requests sent by those devices and in spite of MAC address randomization. Our solution extends previous work that uses Wi-Fi fingerprinting based on the timing information of probe requests. The only additional information we extract from probe requests is the MAC address, making our method minimally privacy-invasive. Our estimation method is also nearly real-time. We conduct several experiments to collect wireless measurements in different static environments and we use these measurements to validate our method. Through an extensive analysis and parameter tuning, we show the robustness of our method.

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