## 1 Introduction
Without a structured method for selection, the effectiveness and efficiency of the solutions can be sacrificed [9; 11]. The challenges regarding hardware selection in edge computing closely mirror those encountered in IoT platform selection, as both require navigating a diverse landscape of heterogeneous options while evaluating multiple criteria such as performance, energy efficiency, cost, and integration [10]. Existing literature on IoT highlights the challenges regarding the hardware selection: For example, [PERSON] et al. [9] emphasized the complexity of IoT platform selection, indicating the challenges faced by companies in selecting the right IoT platform for Industry 4.0 implementation due to variety of available platforms and their distinct characteristics. [PERSON] et al. [11] have also addressed the criticality of choosing suitable platforms considering the technical, social, and organizational aspects. Upon reviewing the literature, we encountered three main approaches that are utilized for hardware selection, which are optimization-based techniques, machine learning-based approaches, and MCDA techniques [11; 12; 13; 14]. Optimization-based techniques are used to optimize specific objectives, such as minimizing cost or energy consumption. However, their limitation lies in their tendency to find local optima rather than global solutions [11]. Machine learning methods, while powerful, rely heavily on large, high-quality datasets, which can be a limitation if the data are limited or noisy. On the other hand, MCDA techniques, such as AHP [15], TOPSIS [16], and VIKOR [17], are widely recognized for their flexibility to work well even on small datasets; numerous techniques have been proposed to determine rankings of objects, attribute weights, and their combinations, and the results can be easily analyzed [11]. MCDA techniques have also been proven to be effective in scenarios involving trade-offs between conflicting criteria [17], making them also well suited for decision-making tasks like edge hardware and AI model selection. Several studies in literature have applied MCDA techniques for hardware selection tasks: For example, [PERSON] et al. [10] have proposed a multi-criteria decision-making framework to assist engineers and technology managers in evaluating and selecting the appropriate IoT hardware, assessing parameters like performance, storage, and energy consumption to match the specific requirements of the IoT deployment. Similarly, [PERSON] et al. 's study [9] has proposed an MCDA approach to guide companies in selecting suitable platforms, evaluating a range of diverse and often conflicting criteria, ensuring that the selection aligns with the specific needs, goals, and constraints of the organization. [PERSON] et al. [11] have extended this approach to agriculture and developed a framework using a modified Multi-Attribute Border Approximation Area Comparison (MABAC) method in a fuzzy environment. Regarding the use of MCDA techniques, one key shortcoming highlighted in the literature is the reliance on a narrow set of criteria during the assessment phase. Most studies on selection tasks [18; 19; 20; 21; 22] have focused on a limited set of criteria. While existing work demonstrates the utility of MCDA for specific cases, there is a need for broader evaluation frameworks or methodologies to address the multi-faceted nature of hardware and AI model selection. The primary work addressed in this study is driven by the lack of a systematic method for the selection of suitable hardware and AI models for edge applications. In this study, we propose a generic method for the evaluation of edge hardware and AI model candidates. The method relies on the ISO25010:2011 quality standard [23], which provides a comprehensive and up-to-date set of quality characteristics and sub-characteristics that support a thorough evaluation of edge hardware and AI models.