## 5 Discussion
### Most Widely Used Public Datasets  
Of the experiments reported from 2016, 22% were performed on public datasets; in 2021 this percentage rises to 39%, most likely due to the COVID-19 pandemic, although up to six papers from these years do not indicate what type of radio map they used. Thanks to these public datasets, researchers were able to provide useful results while continuing to perform experiments during the pandemic. In addition, these public datasets play a key role in research, as they allow researchers to compare different algorithms tested with the same data. In the list, the most commonly used public signal map is UJIndoorLoc [71] in all its variants (different buildings, any floor). It appears in 23 papers, and is clearly the most important, especially in the last two years, when the rest of the maps we found appear only once. Other radio-maps used are IPIN2016 [72], UTSIndoorLoc [73], JUIndoorLoc [74], Rajen Bhatt [75], Cramariuc [76], Alcala Tutorial 2017 (included in UJIndoor), WIFINE [77], UJI Library [78], and Tampere [79]. In Table 1 we provide a summary of different attributes of the public datasets used, while Figure 8 shows the evolution of the use of different public datasets over the years. Note that before 2017 there were no public datasets that met the necessary conditions to be used in simulations, thus, we must recognize the recent contribution of these datasets to this field of research. Because UJIndoorLoc is the most widely used dataset, we performed a deeper analysis into how it has been used by researchers. Figure 7: Evolution of experimental vs. simulated studies. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Public Radio Map** & **Year** & **Size** & **APs** & **rPoints** & **Others** \\ \hline UJIndoorLoc & 2014 & 110,000 m\({}^{2}\) & 520 & 993 & three buildings with four or five floors depending on the building. \\ \hline IPIN2016 & 2016 & 150 m\({}^{2}\) & 168 & 57 & a university corridor \\ \hline UTSIndoorLoc & 2019 & 44,000 m\({}^{2}\) & 589 & 1452 & a building with sixteen floors, including three basement levels \\ \hline JUIndoorLoc & 2019 & 2646 m\({}^{2}\) & 172 & 2646 & faculty rooms, classrooms, seminar rooms, research labs, and corridor \\ \hline Rajen Bhatt & 2019 & 4 rooms & 7 & 1000 & conference room, kitchen, or indoor sports room \\ \hline Cramariuc & 2016 & 2 university building & 663 & 2651 & data divided into two different University buildings. \\ \hline WiFine & 2020 & 9000 m\({}^{2}\) & 436 & 26,418 & based on 260 trajectories \\ \hline UJI Library & 2020 & 308.4 m\({}^{2}\) & 448 & 212 & data taken across fifteen months at the same positions and directions \\ \hline Tampere & 2017 & 22,570 m\({}^{2}\) & 992 & 4648 & 882 rooms on six floors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Public dataset summary. Figure 8: Evolution of the use of public datasets over the years. UJIIndoor Results Analysis  
Due to the large number of articles that performed their tests with UJIIndoorLoc, it is useful to provide a comparison of the different algorithms used on this dataset; results without the mean error have been omitted. Table 2 shows the main papers that used the UJIIndoorLoc dataset. In analysing these papers, we detected different strategies in researchers' methods of treating the original UJIIndoor data, resulting in a mean positioning error much below the baseline. From this analysis, we can conclude that the best result obtained by correctly using training and testing from UJIIndoorLoc is [84], with a mean error of 5.64 m.  
Despite other works reporting lower positioning error results, these results cannot be directly compared to the baseline as their evaluation was restricted to a small area within the full operational area (a building and/or a floor) and/or the evaluation data contained samples from the original training set, as we show in the following paragraphs.