## 6 Discussion  
In this study, we presented a systematic and adaptable method for evaluating and selecting edge hardware and AI models for deployment in edge environments, guided by the ISO/IEC 25010:2011 quality standard. To validate the proposed method, a four-scenario case study focused on UAV operations was conducted, incorporating six fuzzy Multi-Criteria Decision Analysis (MCDA) methods. These methods were used to rank edge hardware alternatives and object detection models based on use case-specific requirements, emphasizing the importance of aligning hardware and software capabilities with operational goals. The quality assessment framework and selection process presented in this study are designed to be flexible and adaptable to different edge devices and AI models, beyond the specific cases considered in the study. The core methodology--comprising Requirements, Specification, Measurement, and Evaluation Layers--is independent of specific hardware or software configurations. Therefore, the process can be applied to a broad range of devices and models by adjusting the quality attributes, metrics, and evaluation methods according to the specific requirements of scenarios. The results of the case study underscore the role of tailoring hardware and AI model selections to specific scenarios: Scenario I demonstrated that low-cost, low-power solutions such as the STM32H7 series are ideal for basic tasks with minimal computational demands. Their affordability, lightweight design, and efficiency make them suitable for resource-constrained UAV applications requiring agility and endurance. Scenario II highlighted the Jetson Nano's capability to deliver high-speed performance in dynamic scenarios, such as real-time obstacle detection, where responsiveness and frame rate are prioritized over high accuracy. Scenario III showcased the Raspberry Pi 4 as a versatile, cost-effective alternative for general-purpose tasks such as environmental monitoring or outdoor inspections, striking a balance between performance, power consumption, and cost. Scenario IV demonstrated that the Jetson Xavier NX, paired with robust AI models like Complex-YOLOv3,exels in high-stakes applications requiring advanced AI capabilities, high accuracy, and moderate inference speed, making it suitable for tasks such as search and rescue or advanced surveillance. These findings illustrate the value of systematically balancing competing trade-offs--such as cost, accuracy, speed, and power consumption--while ensuring compatibility between hardware and software. The deployment of object detection models like YOLO-FastestV2 and Complex-YOLOv3 in these scenarios further reinforces the importance of selecting models that align with the computational capabilities of the hardware and the operational requirements of the use case. To demonstrate the practical relevance of the proposed method, a TensorFlow Lite model was developed and deployed based on the recommendations of Scenario I. The deployment results validated the proposed process, highlighting its ability to guide real-world decisions by systematically addressing stakeholder-defined priorities. Regarding the broader implications of the method, the structured approach proposed in this study offers potential for Edge AI applications beyond UAV context. Its adaptable structure makes it applicable to a wide range of domains, such as smart home systems, robotics, autonomous vehicles, and IoT environments. By following the process flow and leveraging fuzzy MCDA methods, the method helps decision-makers to evaluate trade-offs and achieve optimal balance across multiple competing priorities, such as cost-efficiency, performance, and accuracy. The process flow provided in Section 4 and illustrated in Figure 3 serves as a framework for adopting the method for future case studies. However, it is also important to emphasize the limitations of this analysis. Although the fuzzy MCDA methods provide good assistance for selection processes for the contexts in interest, they are reliant on the selected criteria and weights.