## 6 Conclusions  
In this paper, we have shown an analysis of the use of machine learning for indoor positioning in Wi-Fi based systems. The starting point has been a systematic review, following the PRISMA guidelines, of the current status of the application of deep learning algorithms applied to indoor positioning using Wi-Fi. Information from 119 articles published between 2016 and 2021 has been extracted and analyzed. In total, 161 simulations or experiments were analyzed.  
In this study, we observed a tendency to use Neural Networks in solutions based on the use of Wi-Fi networks. However, we did not find any optimal or standard configuration. In addition to Neural Networks, SVM is widely used as well.  
We noted the predominant use of RSSI Wi-Fi signals, although the studies that focus on the CSI are very promising, and are the ones that have obtained the best accuracy; furthermore, in the last year there has been an increase in the number of articles focusing on the use of this information. The only drawback is the difficulty of accessing this information on a Wi-Fi signal.  
In analyzing the quality of the results, the Mean Error is the most widely used metric, followed by Accuracy (in percentage). In all cases, the articles analyzed in this review provided results followed the ISO/IEC 18305 guidelines.  
Regarding their experiments, we found that most of the papers used empirical results. These papers usually show better results, however, this is generally due to better prepared environments. One important element that we found is that most of the papers prioritize improved results instead of working in a real environment. Thus, although test field sizes range from 6.7 m\({}^{2}\) to 10,000 m\({}^{2}\), many of these experiments are performed in small work spaces with many reference points and/or APs, or in open spaces without walls, which leads to unrealistic results in everyday environments. Likewise, we found studies in which training and validation data are misused by using repeated values for both sets.  
On the other hand, forty papers used public datasets, among which the most popular was UJIIndoorLoc. Using public datasets allows for comparisons to be made between algorithms, as they are tested in the same environment. However, a deep analysis on how UJIIndoorLoc has been used revealed that many authors created their own test and validation data from the training dataset, which leads to overfitting and therefore, to better results than would be obtained with the baseline dataset.  
Figure 9: Size of scenarios used in experiments (in square meters).  
The tables included in this review should be useful for those who want to focus their work based on the size of their work area, choice of machine learning algorithms, and desired accuracy, as well as their choice of the currently most commonly used metrics.  
Conceptualization, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Funding: [PERSON] wants to acknowledge the support of GeoLibero CYTED network. [PERSON] gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under Marie Sklodowska Curie grant agreement No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, [[http://orientate.dsi.uminho.pt](http://orientate.dsi.uminho.pt)]([http://orientate.dsi.uminho.pt](http://orientate.dsi.uminho.pt)) [Accessed on 14 June 2022]).  
Institutional Review Board Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest.