## 8 Conclusions  
The fusion of edge computing and Artificial Intelligence (AI) has given rise to the Edge AI paradigm, which has shown significant usage across various application domains, from autonomous vehicles to smart home systems. Despite its growing adoption, the process of selecting suitable edge hardware and AI models for deployment is challenging due to availability of diverse options and involved trade-offs. This study addresses this challenge by proposing a structured method for evaluating and selecting hardware and AI models tailored for edge environments, guided by the ISO/IEC 25010:2011 quality standard. Through a systematic four-layered framework--Requirements, Specification, Measurement, and Evaluation--the method provided alignment with stakeholder priorities and operational needs, making it applicable to diverse scenarios. The application of the method in four UAV use-cases demonstrated its utility in balancing trade-offs such as cost, power efficiency, speed, and accuracy, and highlighted the importance of tailored hardware-AI model combinations for specific tasks. For example, lightweight setups like STM32H7 with SSD Mobilenet were found suitable for resource-constrained applications, while powerful configurations such as Jetson Xavier NX paired with Complex-YOLOv3 excelled in high-stakes scenarios requiring high accuracy and robustness. We also acknowledged certain limitations in this study, such as the reliance on subjective criteria weighting and potential biases introduced through fuzzy MCDA techniques. To address these issues, expert consultations and statistical analyses were integrated into the weighting process to ensure a reliable evaluation process. To show the practical guidance of the method, we provided an additional experiment which is based on the outcomes of Scenario I. In the experiment, we deployed the ST SSD Mobilenet v1 0.25 model on the STM32H747I-DISCO board, and evaluated the model's performance on the test data of the COCO Person Dataset. Furthermore, we created a TensorFlow Lite model from scratch, trained with the PASCAL VOC 2012 Dataset, which is widely recognized for object detection tasks. Then, we evaluated this model's performance on the validation set of the PASCAL VOC Dataset, and measured its average precision across various categories. It performed better than the pre-trained model with the COCO Dataset. We believe that this study contributes to the field by presenting a systematic approach to a previously ad hoc process, helps reduce inefficiencies and supports decisions on optimal hardware and AI model combinations for edge applications. For future work, we plan to expand the application of the proposed method to various edge domains, such as healthcare and smart cities to show its versatility and relevance in real-world contexts, and to support its generalizability. Conceptualization, M.C.S. ; Methodology, M.C.S. and A.K.T. ; Software, M.C.S. ; Validation, M.C.S. ; Investigation, A.K.T. ; Data curation, M.C.S. ; Writing--original draft, M.C.S. ; Writing--review & editing, A.K.T. ; Supervision, A.K.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability StatementData used in this study are put to an accessible research repository: [[https://zenodo.org/records/14698719](https://zenodo.org/records/14698719)]([https://zenodo.org/records/14698719](https://zenodo.org/records/14698719)) (accessed on 17 December 2024). The authors declare no conflict of interest. \begin{table}