## 2 Related Works
### Study of the State of the Art
The authors suggest that a Flying Ad Hoc Network (FANET) outfitted with image sensors can come in handy whenever extensive monitoring activities must be performed, as well as other tasks like spotting trespassing hunters, illegal farming activities or controlling herd-related operations. To this purpose, a deployment scheme is built where a FANET network monitors a large area of the countryside while collecting data and preserving coverage time and interconnections as optimal as possible, along with a task-sharing protocol among the UAVs that are part of the FANET. Due to a different scope from the one presented in our manuscript, the paper does not provide enough information about other features UAVs should have or how they can be used under a security framework, while our proposal takes these aspects into account and can be used with a collection of UAVs as well. [PERSON] and other researchers describe in [20] how UAVs can be used for animal counting and monitoring via artificial vision. In this case, a Convolutional NeuralNetwork (CNN) is used for cattle counting purposes to detect livestock scattered in the field. The technology used and the system created for these goals prove to be effective, but this piece of research is focused on data visualization techniques rather than the UAV technologies for secure data transmission and storage (aside from the camera used in the UAV itself). [PERSON] et al. [21] put forward their own deployment for visual localization and individual identification of Holstein Friesian cattle via Deep Learning. As it happens in other regions of the world, identification and traceability of cattle has become mandatory due to the kind of legislation enforced. In this case, a R-CNN (Regions with CNN features) adaptation of a network published as part of other already existing network architecture proposals has been used for the purpose of locating and detecting Friesian cows. A Video-based Long-term Recurrent Convolutional Network (LRCN) technique has been used for individual identification. Overall, this piece of work mimics the ones presented before that make use of Deep Learning for localization and identification of cattle, but as it happened before, it is oriented towards different goals than the ones put forward in this manuscript. A similar approach can be observed in [22], where LCRN and Long Short-Term Memory (LSTM) technologies are used for the same kind of purposes. Furthermore, it is also shown in [23] how UAVs can be used to measure spatial proximity, a parameter of major importance when defining social structure, dyadic relationships or grazing and maternal behavior among cattle. UAVs were used to monitor both the location of the individual members of the herd and the identity of cow-calf pairs. To perform the required research, cattle became acclimatized to the UAVs, a procedure that lasted 3 days until cattle got fully adjusted to the presence of the UAVs. An additional period of 4 days was used to collect proximity data. Results showed that cow-calf distance was of around 40 m during daytime and more than 80 m during the evening. These research activities prove that UAVs can be used for measurement purposes whenever a significant degree of accuracy is required, but these activities do not cover other aspects like the security of the information transferred or the components of UAVs that must be settled. Other pieces of literature are oriented towards researching a specific part of the livestock monitoring procedures. For example, [PERSON] et al. [24] have performed a review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles. This and other presented research works show how Deep Learning is a very useful tool for cattle detection or counting, but they refer to an area different from the one covered in this paper, which is oriented towards hardware and software components for a UAV monitoring system and how they are used within a secure framework.