Cited by 52Indoor and Outdoor Localization TechnologiesWireless Networks and ProtocolsBluetooth and Wireless Communication Technologies

The DOI matches its Crossref record; 50 of the 54 references it cites were found in a registry or an index.

Mobile Device Detection Through WiFi Probe Request Analysis

Open access

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1109/access.2019.2925406

Cites

50 of 50 references verified0 citation errors · 0 not checkable

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Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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”

Citations

Cites

Flagged first, then by citations

4 of the 54 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. hailemariam, 2011, Real-time occupancy detection using decision trees with multiple sensor types, Proc Symp Simul Architect Urban Design, 141
  2. gunay, 2016, Detecting occupants’ presence in office spaces: A case study, ESIME, 2016, 1
  3. yoshida, 2015, Estimating the number of people using existing wifi access point in indoor environment, Proc 6th Eur Conf Comput Sci (ECCS), 46
  4. oliveira, 2019, Sherlock

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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
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

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