## 1 Introduction  
The use of outdoor positioning solutions using Global Navigation Satellite Systems (GNSS) technology, such as GPS, GALILEO or GLONASS, is commonplace. Their success lies in the fact that only one receiver (e.g., a cell phone) is needed to obtain the position. However, in closed places (buildings, tunnels, etc.) all of these systems fail, and are unable to obtain a position because the signal cannot penetrate the walls.  
To obtain positioning in indoor environments, a technology different from GNSS is needed. Nevertheless, there is not currently an equivalent universal solution. However, in recent years, there has been important progress in many of the technologies used for indoor positioning, including Inertial Positioning [1], Bluetooth [2], Ultrasound [3], Visible Light [4], Wi-Fi [5], etc. These technologies can be applied either individually or together, in what is known as sensor fusion [6, 7]. In addition to these \"classical\" technologies for indoor positioning, promising approximations have recently appeared, such as 5G [8] and Wi-Fi mmWave [9]. Among these possibilities, Wi-Fi-based solutions are very popular, mainly because the infrastructure required for their deployment is already available everywhere, and if it is not, it can be implemented easily and cheaply. For this reason, there are a large number of items based on this technology and the number is growing all the time. In the last few years, there has been a significant increase in the application of machine learning models to enhance the accuracy of indoor positioning. This large volume of works requires a compilation, ordering, and classification of the results in order to assist researchers in selecting appropriate machine learning models for positioning purposes.  
Thus, this work has two main contributions: (1) a review of papers published between 2016 and 2021 that use machine learning for indoor positioning, reporting information about the algorithms used, type of article (experimental/simulated), number of Access Point (AP) used, number of radio map reference points used, results obtained, type of signal used, and use or non-use of rooms in experiments; and (2) an analysis of how the main dataset, UJIndoorLoc, has been used in those papers along with the main drawbacks detected when using datasets. The selection of papers in this review was performed following the PRISMA guidelines [10].  
This article is an extension of a work presented at the 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021) [11]. Its novel contents include the following:  
* The current work extends the analyzed period to the last five years, analysing a total of 119 published research works, 57 more than in [11];
* An analysis of solutions based on Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) is included;
* A comprehensive analysis of the most widely used public datasets (radio maps) and how they have been integrated in experiments performed by the research community;
* A discussion of the size of the operational areas considered in experiments performed in the reviewed works;
* Extended context, discussion, and conclusions.  
The rest of this review is organized as follows. Section 2 reviews the existing literature related to indoor positioning. Section 3 describes the methodology used in this paper. Section 4 presents the detailed results in the form of a table. The results of the table are analyzed in Section 5. Finally, Section 6 provides conclusions.