## 2 Related Works
### Study of the State-of-the-Art
This piece of research proved that, as shown in our own study, UAVs could be used for purposes that go beyond mere camera visualization and collect data from a collection of different installed sensors if required, but there is no explanation on how a system for mobile network signal measurement could be built, nor the ancillary subsystems required to visualize the collected data and infer information from. Furthermore, no information has been provided on the set of user requirements that would be needed to create this kind of system. Other research works of interest include those shown in [14]. As far as the research in this paper is concerned, the authors of this paper made use of a UAV to perform several operations, such as (a) conducting Air-to-Ground (AG) measurements for UAV vertical flights, (b) the Path Loss Exponent (PLEs) and height impact factor, (c) a proposed novel Autocorrelation Function (ACF) model of shadowing and (d) an analysis of Average Fade Duration (AFD) and Level Crossing Rate (LCR) parameters. To perform the AG measurements, a system composed by a DJI N3 six-rotor UAV (which was equipped with several hardware components used for data collection, like a GPS antenna, a telemetry module, test and antenna transmitters and a battery to feed power to the UAV), a test transmitter, a signal analyser, one laptop, and antennas was built. This research proved that UAVs could be used in an effective manner to gather information regarding channel measurements, but its focus was oriented towards the usability of UAVs as mobile base stations, thus differing from providing a full-fledged interface that could be offered as a user-friendly solution that provides detailed information with regards to mobile network signals. Other kinds of measurement activities can also be carried out by UAVs, as described in [15]. In this case, the system that was developed had two main parts: a UAV platform consisting of an oil-powered unmanned helicopter SU-H2M with a maximum payload of 45 kg and a cruising speed of 60 kilometres per hour, and an aeromagnetic measurement equipment that had five parts: (a) a high-precision potassium pump magnetometer; (b) a three-axes fluxgate manometer; (c) an inertial navigation module; (d) an altimeter; (e) a data collector module with a data processing platform. The results showed that this system could be used to detect anomalies in specific geological locations, and a map with aeromagnetic data could be created with a very high degree of accuracy. As before, this paper proved that UAVs could take measurements in a reliable manner for a plethora of different application domains. However, the scope of this research was focused on aeromagnetic measurements rather than the characteristics of mobile network signals, which is a completely different application domain. In addition, there was no development of a GUI that showed, in a user-friendly manner, the results obtained from the signal. A similar purpose was the focus of the research represented in [16], but instead of monitoring features or parameters related to wireless communications or man-made deployment of technology, it was _Phaeocystis globosa_ alga (defined in the paper as a _\"unique causative species of harmful algal blooms, which can form gelatinous colonies\"_) the element being monitored. blooms of this alga were sought, and their biomass was calculated according to the data collected by the UAV. These parameters became known based on in situ remote sensing reflectance, which was measured over a large distance range. Once the UAV had collected the data, it was used for the purposes described in the reviewed paper. While the scope of this reviewed piece of research was different compared to the one put forward in our paper, it again shows how UAVs can be used for measuring a significant diversity of parameters depending on the sensors they have been equipped with, as well as providing extra access and mobility to regions that could not be accessed with human operators or regular devices. The work shown by [PERSON] et al.