## 3 Dataset Analysis  
In the period between November 2017 and February 2018, the dataset on which our study is based was collected by the Faculty of Applied Engineering of the University of Antwerp [11]. Hardware consisting of a GPS receiver and LoRaWAN end device was installed on about twenty cars from the Antwerp postal service. While the 20 cars drove around in the city center, the location information was sent in a LoRaWAN message. A callback function was configured on the LoRaWAN backend server to forward the payload of each message along with additional network information to the local data server. In the dense urban area explored, 72 LoRaWAN gateways were detected. Figure 2 shows a picture of the Antwerp Urban Area and message locations. The urban dataset contains 130,429 messages and is available at [12]. Every record represents one of the 130,429 messages collected in the urban dataset, see Table 1. In the last four columns appear the receiving time, the spreading factor, latitude and longitude of a message. The previous column shows which of the 72 base stations in the metropolitan area received the message. An RSSI of \(-200\) dB is reported in the cell if the base station does not receive the message. Received Signal Strength Indicator (RSSI) is a measure of the  
Figure 1: LoRaWAN network architecture. Figure 2: LoRaWAN dataset collected in Antwerp city center. power present in the received radio signal. This requires no additional power, hardware, or bandwidth. These properties of RSS measurements make them relatively cheap and easy to implement, making this technique attractive [13]. In previous works, a basic kNN fingerprinting localization technique was used. A parameter sweep was analyzed, varying \(k\) from 1 to 15. Considering that the optimal value of \(k\) was the one which produces the lowest mean location error, it emerged that the optimal value for \(k\) was 11 nearest neighbors. Applying this value of \(k\), the LoRaWAN dataset returned a median error of 273.03 m and a mean error of 398.4 m.  
By reproducing the same approach, it is possible to find the matrix of the centroid locations. Plotting the centroids on a map, as in Figure 3, it emerges that the resolution achievable with this technique is exactly the one indicated above. Calculating the distance of the centroids in the most densely urbanized area, it can be seen, in fact, that the values are around 796 m. This result confirms what we have seen so far, namely that in this densely urban context, using the kNN resolution technique, resolution does not drop below 398 m.  
Is it possible to obtain a mean location error lower than that obtained with the kNN technique using the dataset as input in a neural network designed for deep learning attribute data classification? With the aim of answering this question, we started with a detailed analysis of the densely urbanized area. This area, in the specific case of Antwerp, is contained in a rectangle whose vertices have coordinates between [4 deg20' East, 4 deg27' East] and [51 deg11' North, 51 deg1' North]. We therefore divided the area subtended by the rectangle into subareas. Within each of these subareas, we can place a subset of the original dataset. This allows us to reconstruct the dataset with a further column whose information is related to the subarea which our message belongs to. For each of these subregions, we used a distance-based clustering function for a set of \((x,y)\) coordinates [14]. The function returns clusters in a set of spatial points represented by \((x,y)\) coordinates. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**BS 1** & **BS 2** & **\(\dots\)** & **BS 72** & **RX Time** & **SF** & **Latitude** & **Longitude** \\ \hline \(-200\) & \(-200\) & \(-200\) & \(-200\) & \"2019-01\" & 8 & 51.23399... & 4.42610... \\ \(-200\) & \(-118\) & \(-200\) & \(-97\) & \"2019-01\" & 7 & 51.20718... & 4.40368... \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \"...\" & \(\dots\) & \(\dots\) & \(\dots\) \\ \hline \end{tabular}
\end{table}