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Mobile Device Detection Through WiFi Probe Request Analysis
Open accessAbstract
Estimation of the presence of people in real time is extremely useful for businesses in providing better services while saving money. In this paper, we propose a technique for estimating the number of mobile devices present at a certain place and time, through analysis of WiFi probe requests from smart devices. Our goal is to address the problem through a solution that is immune to Media Access Control (MAC) address randomization strategies. The idea is to make use of information propagated in the environment, without the need to know the real MAC addresses of the devices. A state machine was modeled to detect the arrival, presence, and departure of devices in proximity to the sensors. A hardware prototype was developed for device detection, and its efficiency was evaluated in experiments that involved comparing the results of the proposed method with the manual measurements made by researchers. The proposed method provided very accurate correlations between the number of mobile devices detected and the real number of people in the environment.
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Wi-Fi estimations can never be totally accurate due to no devices, multiple devices, nearby devices, insufficient stay, and missed transmissions.
“An alternative estimation based on Wi-Fi is described in paper [33], since estimations can never be totally accurate due to situations where some people may not carry devices with a Wi-Fi interface or the interfaces are not enabled, some people may have more than one device, devices located in nearby spaces can be detected, the length of stay in the room may be insufficient for the detection of a”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [33]
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Metadata
- Authors
- Oliveira, L., Schneider, D., De Souza, J., & Shen, W.
- Affiliations
- Instituto Federal do Rio de Janeiro (BR)
- Universidade Federal do Rio de Janeiro (BR)
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 98579-98588
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2954160473
- Cited by
- 52
- References
- 54
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 5 · 2023: 11 · 2022: 11 · 2021: 11 · 2020: 5
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Brazilian Research Council (#311865/2017-8)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Bluetooth and Wireless Communication Technologies
- Keywords
- MAC address randomization, occupancy detection, state machine model, hardware prototype
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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