## 5 Discussion
### Most Widely Used Public Datasets
In [41], the author separates the multi-floor and single-floor data to treat them independently, then, from the same dataset, separates 80% for training and 20% for testing. This method can lead to better results, as it is very likely that data taken at almost the same time can be in both the training and test sets. On the contrary, in [80], the authors use the validation component (1111 samples) of the dataset as the test set. The training portion of the dataset is split into training (15,950 samples) and validation (3987 samples) subsets based on an 80:20 split. This is a good practice, as the authors do not mix training and test data. In [82], the authors selected a subset of the original dataset. In this case, the authors focus on data from only one part of the dataset (building 0) and only choose the strongest RSSI signals. Literally, \"In particular, the Building 0 from UJIIndoorLoc dataset is chosen to evaluate EdgeLoc and the top-40 APs (out of a total 520 APs) are selected.\" Although AP selection can be performed as an optimisation step in this method, restricting evaluation to just one building makes the results not directly comparable with the baseline method or with other methods that used the full operational area. The authors in [29] do not specify how the data were used. Literally, \"In order to better verify the performance of the algorithm, we also conducted experiments on another widely used positioning dataset UJIIndoorLoc.\" However, this algorithm requires a set of evaluation paths to asses the proposed algorithm, which is not provided in the original UJIIndoorLoc dataset. The full details about the data points used and how the evaluation paths were generated are lacking, i.e., the information provided does not enable reproducibility/replicability of the results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Art** & **Year** & **mAlg** & **mError** \\ \hline
[41] & 2021 & DRL & 3.06 m \\ \hline
[80] & 2021 & CHISEL (CNN) & 6.95 m \\ \hline
[21] & 2021 & CNN & 12.4 m \\ \hline
[81] & 2021 & DeepLocBox (NN) & 9.07 m \\ \hline
[82] & 2021 & Edgeloc(CapsNet) & 7.93 m \\ \hline
[29] & 2020 & RNN & 4.91 m \\ \hline
[83] & 2019 & RNN & 4.2 m \\ \hline
[84] & 2019 & MLP & 5.64 m \\ \hline
[85] & 2018 & RDF & 6.72 m \\ \hline
[86] & 2018 & CNN—Single RSS vector & 10.25 m \\  & & CNN—Time Series & 2.77 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Articles that used the UJIIndoorLoc dataset. In [83], the authors use the UJIIndoorLoc as an additional experiment alongside their main work. However, the dataset was restricted to a small subset of the UJIIndoorLoc dataset. Literally, \"The database from two random phone users (phone id: 13 and 14) in two different buildings (building id: 0 and 1) are used.\" In this way, the data to be analyzed and trained on are much simpler and similar, resulting in optimistic performance compared to the baseline method and other solutions using the whole UJIIndoorLoc dataset. In [85], the authors used two datasets, including UJIIndoorLoc, to assess their proposed model. Despite providing details about the other dataset, they only mention that the UJIIndoorLoc dataset had 21,048 Wi-Fi fingerprints. It seems that the training and evaluation sets were merged into a common superset, which was later split by building ID in order to evaluate the model in three scenarios (buildings). Each of the three sets were split with a ratio of 70:30 to train and evaluate the proposed RDF ensembles. In [86], the authors randomly split the training set into training and validation sets with a ratio of 80:20. Then, the resulting solution based on CNN was tested over the 1111 evaluation samples, as in the original dataset for the models based on single RSS readings.