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TitleComprehensive analysis of applied machine learning in indoor positioning based on Wi-Fi: an extended systematic review
Author(s)Bellavista-Parent, Vladimir
Torres-Sospedra, Joaquín
Perez-Navarro, Antoni
Wi-Fi radio map
Machine learning
Issue dateJun-2022
CitationBellavista-Parent, V.; Torres-Sospedra, J.; Pérez-Navarro, A. Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review. Sensors 2022, 22, 4622.
Abstract(s)Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines.
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals

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