A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques
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, ha...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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MDPI AG
2022-06-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/22/13/4925 |
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author | Daniele Atzeni Davide Bacciu Daniele Mazzei Giuseppe Prencipe |
author_facet | Daniele Atzeni Davide Bacciu Daniele Mazzei Giuseppe Prencipe |
author_sort | Daniele Atzeni |
collection | DOAJ |
description | 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. |
first_indexed | 2024-03-09T12:34:18Z |
format | Article |
id | doaj.art-d9fc88c0feed469e856d8c49a6bd0ad8 |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-03-09T12:34:18Z |
publishDate | 2022-06-01 |
publisher | MDPI AG |
record_format | Article |
series | Sensors |
spelling | doaj.art-d9fc88c0feed469e856d8c49a6bd0ad82023-11-30T22:27:26ZengMDPI AGSensors1424-82202022-06-012213492510.3390/s22134925A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling TechniquesDaniele Atzeni0Davide Bacciu1Daniele Mazzei2Giuseppe Prencipe3Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, ItalyDepartment of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, ItalyDepartment of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, ItalyDepartment of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, ItalyWireless 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.https://www.mdpi.com/1424-8220/22/13/4925machine learningWi-FiBERTopictopic modelingartificial intelligence |
spellingShingle | Daniele Atzeni Davide Bacciu Daniele Mazzei Giuseppe Prencipe A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques Sensors machine learning Wi-Fi BERTopic topic modeling artificial intelligence |
title | A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques |
title_full | A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques |
title_fullStr | A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques |
title_full_unstemmed | A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques |
title_short | A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques |
title_sort | systematic review of wi fi and machine learning integration with topic modeling techniques |
topic | machine learning Wi-Fi BERTopic topic modeling artificial intelligence |
url | https://www.mdpi.com/1424-8220/22/13/4925 |
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