## 1 Introduction  
The growing interest of the telecommunications market for IoT technologies is driving research to develop different Low-Power Wide-Area Network (LPWAN) standards. Simply put, LPWAN should be to IoT what WiFi is to consumer networks. Base stations provide (very) wide radio coverage and adjust transmit power, transmit rate, modulation, duty cycle, etc., so that end devices experience little energy consumption from the connection [1]. Ultralow power consumption, as well as ubiquitous outdoor and indoor connectivity, are fundamental aspects to ensure that the network of IoT devices is reliable over the years. To ensure smooth operations on IoT networks, it is necessary to take into account various elements such as network topology, modulation techniques, complexity of the hardware, the use of the radio spectrum and regulations [2]. From this point of view, it follows that context awareness is a key element in IoT applications [3]. In order to set up context awareness, device location should be determined with minimal location error. The simplest way to achieve this would be to use the GPS tracker. Unfortunately, however, the GPS receiver can consume up to 50 mA when detecting the position [4]. Added to this is the fact that, once the position has been obtained, it is necessary to transmit it to the gateway, and this further step produces an additional energy consumption. A further element to consider is the high accuracy of a GPS measurement, an accuracy that is often not required in an IoT sensor network. So, in the face of a high energy consumption, we would have an excessively detailed measure in the context under analysis. A particularly interesting technique has been developed in [5], in which a simplified implementation of interferometry is presented, obtaining high accuracies. This technique does not require additional hardware, but it cannot be implemented with all communication devices, as it strongly depends on the modulation mechanisms used and the freedoms left to users. Wireless localization based on LPWAN communication is therefore an interesting alternative for localization in low-powernetworks [6]. These techniques estimate transceiver location by analyzing the physical characteristics of the transmission link, such as received signal strength (RSS) values, packet arrival time information and so on [7]. This paper aims to verify if, by applying deep learning methodologies to fingerprint techniques, it is possible to obtain interesting results in terms of minimizing the localization error. The performance of fingerprint-based methods depends on the number of reference points (RP) per room unit. However, since RSS measurements are onerous and time-consuming, increasing the number of RPs increases the positioning cost [8]. The remainder of the paper is structured as follows. Section 2 describes the LoRaWan standard used to collect the dataset. Section 3 shows how the dataset has been built. Section 4 illustrates the machine learning approach that we used to estimate the location of end devices. Section 5 shows the results of our technique. In Section 6, we discuss these results. Finally, Section 7 shows the conclusions and the intended future work.