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Algorithmic Frontiers for Mobile Client Monitoring in the Age of Data Protection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (11 September 2023) | Viewed by 4420

Special Issue Editors

Dr. Iness Ahriz

E-Mail Website
Guest Editor
Conservatoire National des Arts et Métiers (CNAM), Paris, France
Interests: indoor localization; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bruce Denby

E-Mail Website
Guest Editor
Electronics Department, Sorbonne Université, 75005 Paris, France
Interests: signal processing; telecommunications; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the adoption of the General Data Protection Regulation (GDPR) by the European Union (EU) in 2018—and similar rulings worldwide—many mobile client monitoring tools used by commercial and public organizations have become unavailable due to new definitions of what constitutes "private" information. For example, device-specific medium access control (MAC) addresses necessary for the functioning of wireless sensor networks—until now routinely used for client counting and localisation—are under GDPR considered private information and may no longer be stored after reception of a frame. Thus, although GDPR liberates consumers from certain "predatory" monitoring practices, it at the same time undermines legitimate management procedures such as: audience monitoring, commercial footfall sensor records, and crowd control and localisation for commercial and safety concerns. Today, the bulk of the literature addressing the impact of GDPR concerns privacy protection protocols for the modern technologies, verification of GDPR compliance, or designing for data protection. Our Special Issue has a different perspective: encouraging novel algorithmic developments to assure (or restore) the viability of legitimate client monitoring practices—essential for efficient commercial development, personal safety, and system security—within a reduced input space defined by the new data protection directives. Contributions are solicited on topics such as:

  • New algorithms for audience monitoring and crowd counting in the age of GDPR;
  • New, privacy-preserving client and crowd localisation techniques;
  • Novel GDPR-compliant commercial footfall monitoring techniques;
  • Novel GDPR-compliant data analytics for WiFi and other wireless sensor networks;
  • Signal processing approaches to GDPR-compliant client monitoring in wireless networks;
  • New hardware solutions for GDPR-compliant client monitoring in wireless networks;
  • Impact of the Internet of Things (IoT) in privacy-preserving client data analytics;
  • Related areas.

Prof. Dr. Bruce Denby
Dr. Iness Ahriz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

31 pages, 12682 KiB  
Article
Tools for Ground-Truth-Free Passive Client Density Mapping in MAC-Randomized Outdoor WiFi Networks
by Feifei Yang, Iness Ahriz and Bruce Denby
Sensors 2023, 23(13), 6142; https://doi.org/10.3390/s23136142 - 4 Jul 2023
Viewed by 1599
Abstract
In the past few years, data privacy legislation has hampered the ability of WiFi network operators to count and map client activity for commercial and security purposes. Indeed, since client device MAC devices are now randomized at each transmission, aggregating client activity using [...] Read more.
In the past few years, data privacy legislation has hampered the ability of WiFi network operators to count and map client activity for commercial and security purposes. Indeed, since client device MAC devices are now randomized at each transmission, aggregating client activity using management frames such as Probe Requests, as has been common practice in the past, becomes problematic. Recently, researchers have demonstrated that, statistically, client counts are roughly proportional to raw Probe Request counts, thus somewhat alleviating the client counting problem, even if, in most cases, ground truth measurements from alternate sensors such as cameras are necessary to establish this proportionality. Nevertheless, localizing randomized MAC clients at a network site is currently an unsolved problem. In this work, we propose a set of nine tools for extending the proportionality between client counts and Probe Requests to the mapping of client densities in real-world outdoor WiFi networks without the need for ground truth measurements. The purpose of the proposed toolkit is to transform raw, randomized MAC Probe Request counts into a density map calibrated to an estimated number of clients at each position. Full article
(This article belongs to the Special Issue Algorithmic Frontiers for Mobile Client Monitoring in the Age of Data Protection)
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14 pages, 2966 KiB  
Article
Statistical Approach to Estimating Audience from MAC-Randomized WiFi Probe Requests
by Feifei Yang, Iness Ahriz and Bruce Denby
Sensors 2022, 22(22), 8679; https://doi.org/10.3390/s22228679 - 10 Nov 2022
Cited by 5 | Viewed by 2239
Abstract
In the past few years, the ability of wireless network operators to monitor audience using control frames emitted by client devices has been compromised, both by legislation treating client MAC addresses as private information and by the difficulty of distinguishing genuine client frames [...] Read more.
In the past few years, the ability of wireless network operators to monitor audience using control frames emitted by client devices has been compromised, both by legislation treating client MAC addresses as private information and by the difficulty of distinguishing genuine client frames from those arising from the Internet of Things or from certain enhanced services. Here, a deterministic model, based on characteristics of human activity and on seasonal trends, is used to reveal underlying client statistics in raw MAC-randomized WiFi Probe Request data. The method proposes a candidate conversion factor, X, between probe request counts and the client population, which offers plausible predictions on real-world datasets. Full article
(This article belongs to the Special Issue Algorithmic Frontiers for Mobile Client Monitoring in the Age of Data Protection)
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