## 5 Discussion  
This complete review consisted concretely of 202 total references (since some references are only exemplary/explanatory) of which there were 101 references (50%) for the control subsystem, 41 references (20.3%) for sensory, 32 references (15.84%) for the communication subsystem, 24 references (11.88%) for the computing subsystem, and 4 references (1.98%) for the power subsystem. Under the control subsystem, there were 39 references for strategy techniques (38.62% of the total references, particularly within this set), 9 references (8.91%) for path planning/obstacles avoidance, 14 references (13.86%) for localization schemes, 28 references (27.72%) for target tracking, and 11 references (10.89%) for payload integration and control. Concerning the computing subsystem, 11 references (45.83% of the total, particularly within this set) were dedicated to SBCs, whereas 13 references (54.17%) were for SoM. For the communication subsystem, 11 references (34.37% of total references, particularly within this set) were for LoRa, 9 references (28.13%) for Wi-Fi, 7 references (21.87%) for BLE, and 5 references (15.63%) for LTE-M. In the sensory subsystem, 13 references (31.70% of total references, particularly within this set) were for environmental sensors, 6 references (14.64%) for vision sensors, and 22 references (53.66%) for position sensors. Lastly, four different power sources were classified in the final power subsystem, with each referring to a single reference (25% of total references, particularly within this set). This information is shown in Figure 11.  
Most of the reviewed references refer to different control strategies, since such criteria are directly linked to overall flight stability and success. The employed algorithms for navigation, target tracking, localization, and obstacle avoidance represent the backbone that output modules (i.e., actuators) rely on, in relation with the data acquired through the sensors. Beginning with the vision-based navigation strategies, they pose a challenge on the computing subsystem, specifically concerning the GPU, in order to fully extract the graphical potential behind such strategies (i.e., high-resolution images with precise BRF), an SBC with high GPU capability (e.g., NVIDIA Jetson series) must be employed. In addition to financial burdens, the processing of image-based data incorporates extra  
Figure 10: Droneport schematic representation [299]. programming complexities that are also naturally accumulated by AI-based techniques. The good part of the latter is that they allow for more autonomous UAV processes. In the context of target tracking, the YOLO algorithm with its relative successors (i.e., up to YOLOv8), have shown popularity in adaptation for computing an object's correlative and class probabilities. With every new version of YOLO comes major improvements in terms of knowledge accuracy with respect to its predecessors. For the part of the computing subsystem, the Qualcomm Snapdragon showed superiority with respect to all other SoM/SBCs, mainly regarding its RAM size, integrated GPU, built-in connectivity modules, and ability to be programmed in different languages. From another perspective, according to the data presented in Table 7, LoRa technology is the optimum selection for UAV flights requiring long range of communication (up to 20 km outdoors), with the highest data rate (up to 300 kbps). However, a trade-off must be made between its RAM size and transmission power to better fit an application (i.e., the 32 kB of RN2483 imposes a loss of 2 dBm in the transmission power). The golden rule concerning the sensory part corresponds to the following: the more sensors installed in a UAV, the better its environmental/internal awareness, and the more burden is then created on the level of the computing subsystem. Some sensors, such as temperature sensors, are unavoidable since their output data is highly influential on flight safety and stability.