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A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques
Daniele Atzeni1, Davide Bacciu1, Daniele Mazzei1
1Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy.
Sensors (Basel, Switzerland)
|July 9, 2022
Summary
This review explores the synergy between Wi-Fi sensing and Machine Learning (ML). It details how Wi-Fi advancements impact ML-driven sensing applications and algorithm selection.
Area of Science:
- Computer Science
- Electrical Engineering
- Data Science
Background:
- Wireless networks, particularly Wi-Fi, are integral to modern life, supporting numerous devices.
- The proliferation of mobile devices and the Internet of Things (IoT) generates vast amounts of data.
- Machine Learning (ML) excels at analyzing high-velocity data streams.
Purpose of the Study:
- To systematically review the interplay between Wi-Fi technology and Machine Learning.
- To understand the evolution of this interaction over time.
- To identify current applications and future trends.
Main Methods:
- Systematic literature review using Scopus, Web of Science, and IEEE Xplore.
- Application of BERTopic, a topic modeling technique, for analyzing retrieved abstracts.
- Cluster inspection and statistical analysis for topic interpretation.
Main Results:
- Identified diverse applications of Wi-Fi sensing.
- Cataloged a variety of Machine Learning algorithms employed for Wi-Fi sensing.
- Documented the influence of Wi-Fi technological progress on sensing capabilities and ML model choices.
Conclusions:
- Wi-Fi sensing is a rapidly evolving field driven by technological advancements.
- Machine Learning is crucial for unlocking the potential of Wi-Fi sensing data.
- The choice of ML algorithms is closely tied to Wi-Fi infrastructure and application requirements.
