Automatic Attendance Taking: A Proof of Concept on Privacy Concerns in 802.11 MAC Address Probing

Aun, Yichiet; Gan, Ming-Lee; Khaw, Yen-Min Jasmina

doi:10.1007/978-981-15-2693-0_20

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…In fact, only 53 articles contain the words "probe", "mac addr", or "randomized", with a total 597 of citations (for an average of 11.3 citations for each paper). Moreover, several of them focuses on studying the real effects of MAC randomization [93,94] or device de-anonymization [95,96]. There are in the literature some works that leverage PRs, e.g., in crowd detection [97] and behavior [98] or device classification [99], but we believe that there is still room for improvement.…”

Section: Rqmentioning

confidence: 99%

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Atzeni

Bacciu

Mazzei

et al. 2022

Sensors

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Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.

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