Paper Info
Title
A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques
Abstract
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.
Year
DOI
Venue
2022
10.3390/s22134925
SENSORS
Keywords
DocType
Volume
machine learning, Wi-Fi, BERTopic, topic modeling, artificial intelligence
Journal
22
Issue
ISSN
Citations 
13
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Daniele Atzeni100.34
Davide Bacciu200.34
Daniele Mazzei39915.00
Giuseppe Prencipe400.34