## 2 Related Works
### Study of the State of the Art  
It is mentioned in [10] how an algorithm can be used to distribute a set of UAVs in an open space to maximize the area covered by a group of UAVs. The authors of the paper rely on GPS collars used by individual cattle to advertise their position, which is transmitted to the UAVs. Afterwards, a standard K-Means clustering algorithm is used to determine the optimal distribution of the UAVs deployed in the countryside. This paper justifies the usefulness of utilizing a plethora of UAVs to monitor cattle and how a standard K-Means clustering algorithm can be utilized for such purpose, but its scope is different from the one that is sought in this manuscript, as area coverage rather than security is the main topic of the reviewed manuscript. [PERSON] et al. [11] describe on their own manuscript an application consisting of a real-time mobile system for cattle tracking that makes use of video captured from a UAV. In this case, the authors explore the advantages of using UAVs in large spaces when requested by legislative reasons. They carry on by explaining the framework that has been used to create an iOS application able to send commands to a UAV from the manufacturer DJI, which in turn is capturing videos and images across the pasture. These data are sent afterwards to a server where processing (segmentation and counting) is performed on them. Overall, the authors' work is impressive in the sense that a mobile application has been built with the capability of sending data to a Deep Learning-capable server where data processing can be done, but due to a different scope sought it offers little information about what security measurements are taking place, or what kind of features are expected from the drones used. Furthermore, [PERSON] et al. [12] have performed research activities in determining how to round up several animals by using UAVs. The authors of this paper described how they make use of UAVs that mimic the noise perform by predators so that livestock can be gathered and guided into their pen as fast as possible. After introducing how UAV technology has improved to the point that it is possible to use this unmanned vehicle in the application domain of Smart Farming, the authors describe a mission scenario where a group of UAVs give chase to four animals with GPS collar tags attached to them. MATLAB/Simulink models are used for simulations on the strategies that should be followed by the UAVs. Overall, this piece of research focus on objectives that resemble the ones that we described in our own manuscript, but the results obtained are based on simulations rather than testing with actual cattle, whereas our solution considers the latter as well. Additionally, and as it happens in most of the other cases, security is not prominently considered in the research. It is also described in [13] how Long-Range Wide-Area Network communications (LoRaWAN) can be used to monitor large scale livestock as an activity to perform in rural farms. The authors regard LoRaWAN as one technology that enables the development of the Internet of Drones [14] and carry on describing how a farm-oriented holistic monitoring system can be built with the usage of UAVs, LoRaWAN, Low-Power Wide-Area Networks (LPWANs) and other IoT-related technologies. They also display the main components of the modified LoRaWAN multi-channel gateway produced to make possible the different communications among the integrated parts of the heterogeneous system used for the monitoring activities. The authors, though, have a scope that is different from the one put forward in the manuscript, as their main target is the monitoring water supply in and consumption by livestock, rather than the components required for the monitoring of the livestock themselves. Security is another feature that has not been included or developed in the manuscript either. Another piece of research with equivalent goals is the one presented in [15] where large animals-specifically, groups of yaks- are monitored via UAVs with an hourly spatial distribution.