## 4 Avinomics Assessment
### Computing
#### (b)  
SoM  
A SoM reflects an integrated computing platform encompassing the essential component of a computer (e.g., CPU, RAM, storage, etc.) onto a single module, simplifying the build of larger embedded systems. While both SBCs and SoM serve for similar purposes in embedded systems (e.g., flight control of a UAV), they differ in terms of the form factor and design architecture. On one hand, SBCs come packaged with all essential components, which makes them standalone computing systems, thus neglecting any need for additional hardware. A SoM, on the other hand, cannot directly connect to peripherals because it typically does not have I/O sockets [208]. For UAV control, different SoMs can be used such as NXP I.MX8M [209], Rockchip RK3399 [210], Qualcomm Snapdragon [211], and STM32 [212]. Regarding the work in reference [213], NXP I.MX8M provides a custom AI video-processing-focused hardware platform for the deployment of DL models. By integrating a Neural Co-Processing Unit (NCU), it enables the process of 2.3 tera operations  
Figure 6: Proposed UAV object detection system based on NVIDIA Jetson Nano [207]. per second, hence considerably reducing the processing time for the implemented DL models used in smart cities' smart-camera-systems. Similarly, in [214], Rockchip RK3399 serves as the SoM for video data (recorded in real time by a UAV) processing, providing more speed and prohibiting delays in video transmission in 5G networks. For the purpose of UAV safety landing in a GPS-denied environment, the work in [215] utilized a \"remote-marker-based\" tracking algorithm that is implemented on a Qualcomm Snapdragon SoM. The developed CNN \"LightDenseYolo\" algorithm, by means of an embedded Qualcomm Adreno 540 GPU, extracts features from an input image. This extraction can be used to predict a marker's location by a visible light camera sensor installed on the UAV. The suggested methodology outperforms state-of-art UAV object trackers [215]. Concerning a different approach than in the previously mentioned works, the work in [216] emphasized monitoring a mobile network (i.e., performing testing and measurements) by means of a UAV through the Galileo satellite network. The proposed software runs on a Qualcomm Snapdragon SoM (embedded in a Xiaomi mi 10 lite), enabling effective and accurate remote extraction of mobile network data (e.g., signals levels, data logging, information visualization, etc.). The authors of the reference [217] take a different perspective, based on an improved algorithm involving greater immunity versus cyber attacks in the Internet of Drones (IoD) on a Qualcomm Snapdragon SoM. Their results indicate a stubborn protocol with good resistance in front of security attacks of a swarm of drones used for smart city real-time data collection. Alternatively, in reference [218], the aim is to rescue injured humans swiftly by means of mission choice-driven UAV swarm cooperation. After the transfer of a suspected target's location to a self-organizing network, a bio-radar UAV rechecks for any survivors via a respiratory characteristic-based algorithm that is processed by a STM32 SoM. The data packets sent by the STM32 eventually allow the emergency supplied drop-off by means of a nearby emergency UAV according to the location/vital signs of the target, showing good success and accuracy. Specifically, the STM-based SoM finds various applications in UAV processes. For instance, aside from injured human targeting and help, STM32 succeeds in the analysis of a quadcopter's stability, first, by reading data of angular velocity and acceleration. Then, by supplying Pulse Width Modulation (PWM) signals to an Electronic Speed Control (ESC), the brushless DC motors are correspondingly regulated, providing more controllability and a reduction in the response time of the UAV [219].