## 3 Related Work  
In this section, firstly, the studies that have examined the evaluation and selection of edge devices and AI models are presented. Then, our contribution with this study is emphasized.  
[PERSON] et al. [50] studied selecting suitable hardware for UAVs. They proposed a decision-making approach using the Analytic Hierarchy Process (AHP) method to assess hardware alternatives, and the ISO 25000 family (SQUARE) [23] to evaluate software quality, considering computational performance, size, weight, and power constraints. [PERSON] et al. [8] studied analyzing the edge computing device behavior with embedded AI models, utilizing devices such as Raspberry Pi 4 and Google Dev Coral. They measured the inference time, energy consumption, RAM, and CPU usage. [PERSON] et al. [24] presented a review of Edge AI applications, where they focused on different categories of hardware and software options that can be useful for the selection process. Regarding hardware evaluation and development boards, [PERSON] et al. [51] provided an analysis of development boards that can run AI algorithms at the edge. Their study provided various hardware options and their capabilities, which can be beneficial for selecting the appropriate hardware considering the performance and application requirements. Regarding machine learning and edge device implementation, [PERSON] et al. [52] reviewed models, architectures, and requirements for implementing edge machine learning on IoT devices. [PERSON] et al. [7] evaluated commercial edge devices, such as Raspberry Pi 3B and Jetson TX2, using popular frameworks and Convolutional Neural Networks (CNNs), and examined the impact of various frameworks and software optimizations on device performance. Regarding benchmarking and performance evaluation, various benchmarking (e.g., EdgeFaasBench [53], the YOLO benchmark [54], DL models benchmark [55]) are conducted to evaluate performance metrics such as response times, accuracy, Frame Per Second (FPS) on various edge devices such as Google Coral, NVIDIA Jetson Nano, and the Raspberry Pi series.  
The following shortcomings in the literature have motivated us to conduct this study:* Most of the studies in the literature e.g., [7, 50] have primarily focused on performance metrics and specific hardware and software combinations for Edge AI applications.
* There are a limited number of studies that rely on standards such as ISO 25010 for the evaluation and selection process of edge hardware and AI models. Most of the studies have conducted benchmarking and focused on specific performance metrics (e.g., inference time, power consumption) for specific hardware, e.g., [53, 54, 55].
* In terms of evaluation scope, the studies in the literature, e.g., [53, 54, 55], are often centered around benchmarking on specific devices (e.g., development boards such as the NVIDIA family).
* A limited number of studies have utilized MCDA techniques for the selection of edge hardware or AI models. Moreover, these studies have considered a limited number of assessment criteria [9].
* We have not encountered studies that employ fuzzy MCDA techniques for the evaluation and selection of edge hardware and AI models, assessment criteria of which may require considering uncertainties.  
Different from the studies in the literature, and to the best of our knowledge, there is no comprehensive, generic method or framework for evaluating and selecting edge hardware and AI models based on a quality standard. Our study presents an adaptable method for the evaluation and selection of edge hardware and AI models relying on the ISO 25010:2011 quality standard, which provides a set of quality characteristics and sub-characteristics, supporting a thorough evaluation process. Unlike the previous works that primarily focused on performance benchmarking for specific edge devices (e.g., Raspberry Pi 4, NVIDIA Jetson Nano), our framework provides an adaptable structure that that can integrate quality attributes from ISO 25010:2011 to enable a comprehensive evaluation method.