## 8 Conclusions
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Metric Category** & **Evaluation Indicator** & **Description** & **Application Scenarios** & **Device Constraints Addressed** \\ \hline \multirow{6}{*}{Computational Efficiency [79; 80]} & Frames Per Second (FPS) & Measures real-time processing speed of inference tasks. & Real-time video analytics & Processing under low latency \\ \cline{2-5}  & Latency (End-to-End Inference Time) & Total time for data input to output prediction. & Healthcare AI systems, Robotics & Low-latency or time-sensitive tasks \\ \cline{2-5}  & Model Throughput (Inference per Second) & Number of inferences the model can handle per unit time. & Batch processing, industrial use & Scaling with computational constraints \\ \hline Resource Utilization [79; 80] & CPU/GPU/TPU Utilization & Tracks hardware usage during inference. & Resource monitoring in IoT & Avoids overloading hardware \\ \hline \multirow{6}{*}{Memory (Watts/Inferences per Watt) [71]} & Power Consumption (Watts/Inferences per Watt) [71] & Measures energy efficiency of the device. & Battery-powered drones, Edge AI & Power-efficient operations \\ \cline{2-5}  & Memory Utilization (RAM/Flash) & Evaluates memory footprint for models and inputs. & Embedded AI, wearable devices & Limited memory capacity \\ \hline \multirow{6}{*}{Adaptability} & Transfer Learning & Assesses the ability to fine-tune models for new tasks with limited data. & Personalized recommendations & Adaptation to new environments or datasets \\ \cline{2-5}  & Scalability [65] & Tests how models and hardware perform with varying workloads. & Distributed Edge AI systems & Dynamic workload adjustment \\ \cline{2-5}  & Multi-Model Support [80] & Ability to run multiple models concurrently or in parallel. & Smart city applications & Support for diverse use cases \\ \hline \multirow{6}{*}{Robustness} & Mean Time Between & Evaluates hardware reliability under prolonged usage. & Mission-critical drones, IoT devices & Reliability for long-term deployments \\ \cline{2-5}  & Environmental & Performance under harsh environmental conditions. & Outdoor drones, industrial IoT & Operates in extreme conditions \\ \cline{2-5}  & Fault Tolerance [84; 85] & Ability to recover from hardware or software failures. & Autonomous vehicles & High reliability under unexpected failures \\ \hline \hline \end{tabular}   
* Specific evaluation indicators across domains such as healthcare and autonomous vehicles can be retrieved from device vendors’ or general benchmarking web pages e.g., [79; 80]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model ID** & **Model** & **Resolution** & **Objects** & **mAP** & **FPS** \\ \hline EdgeSW\_Scenario\_1 & NanoDet & \(320\times 320\) & \(80\) & \(20.6\) & \(26.2\) \\ \hline EdgeSW\_Scenario\_2 & NanoDet Plus & \(416\times 416\) & \(80\) & \(30.4\) & \(18.5\) \\ \hline EdgeSW\_Scenario\_3 & YOLOFastestV2 & \(352\times 352\) & \(80\) & \(24.1\) & \(38.4\) \\ \hline EdgeSW\_Scenario\_4 & YOLOv2 & \(416\times 416\) & \(20\) & \(19.2\) & \(10.1\) \\ \hline EdgeSW\_Scenario\_5 & YOLOv3 & \(352\times 352\) (tiny) & \(20\) & \(16.6\) & \(17.7\) \\ \hline EdgeSW\_Scenario\_6 & YOLOv4 Darknet & \(416\times 416\) (tiny) & \(80\) & \(21.7\) & \(16.5\) \\ \hline EdgeSW\_Scenario\_7 & YOLOv4 & \(608\times 608\) (full) & \(80\) & \(45.3\) & \(1.3\) \\ \hline EdgeSW\_Scenario\_8 & YOLOv5 & \(640\times 640\) (small) & \(80\) & \(22.5\) & \(5.0\) \\ \hline EdgeSW\_Scenario\_9 & YOLOv6 & \(640\times 640\) (nano) & \(80\) & \(35.0\) & \(10.5\) \\ \hline EdgeSW\_Scenario\_10 & YOLOv7 & \(640\times 640\) (tiny) & \(80\) & \(38.7\) & \(8.5\) \\ \hline EdgeSW\_Scenario\_11 & YOLOx & \(416\times 416\) (nano) & \(80\) & \(25.8\) & \(22.6\) \\ \hline EdgeSW\_Scenario\_12 & YOLOx & \(416\times 416\) (tiny) & \(80\) & \(32.8\) & \(11.35\) \\ \hline EdgeSW\_Scenario\_13 & YOLOx & \(640\times 640\) (small) & \(80\) & \(40.5\) & \(3.65\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Scenario II, AI model options.