## 2 Background
### Multi-Criteria Decision Analysis (MCDA) Methods  
Many daily problems are intricate and require evaluating several parameters to come up to a final decision. MCDA methods are applied to facilitate the identification of rational solutions [38]; they are employed to develop Decision Support Systems (DSS) [38; 39] in various fields such as supplier selection [38; 40], location choice [38; 41], industry [38; 42], and healthcare [38; 43]. In many real-world scenarios, we need to make rational decisions from a range of options. However, real-world problems often involve uncertainties that complicate this process. MCDA techniques offer valuable assistance for these issues. Various evaluation techniques, which are based on fuzzy logic and known as fuzzy MCDA techniques [44; 45], have been developed to facilitate the efficient and effective assessment of decision alternatives in uncertain conditions. Fuzzy MCDA techniques are also well suited to the problem of selecting suitable edge hardware and AI models for edge applications for the following reasons:  
* Handling the complexity and uncertainty in decision parameters: Edge hardware selection involves assessing various criteria, many of which are subjective, uncertain, or difficult to quantify accurately. For example, while supported AI frameworks or cost might have clear numerical values, criteria like processing efficiency or power consumption may involve ranges of values. In this regard, fuzzy MCDA techniques allow decision-makers to handle these uncertainties by using fuzzy sets, such as Triangular Fuzzy Numbers (TFN), which represent values as ranges instead of precise numbers [38]. * Weighting and Prioritizing Criteria: Different applications may prioritize certain criteria over others. For example, in battery-powered drones (MAVs), power consumption may be weighted higher than processing power, or similarly, in an industrial setting, reliability and processing power may take precedence over cost. Fuzzy MCDA techniques enable decision-makers to define weighted importance for each criterion and ensure that the chosen option aligns with the specific goals and the requirements of the application. * Multi-dimensional trade-offs: In selecting edge hardware, trade-offs often arise due to conflicting objectives (e.g., maximizing performance while minimizing power consumption and cost). These trade-offs can be difficult to handle with traditional methods since they require balancing multiple dimensions simultaneously. Fuzzy MCDA techniques are well suited for handling such multi-dimensional trade-offs. They provide a structured way to evaluate different alternatives, considering both profit criteria (those that should be maximized, like performance) and cost criteria (those that should be minimized, like power consumption or cost). By this way, decision-makers can ensure that the trade-offs are balanced appropriately based on the context. In this study, six fuzzy MCDA methods are applied both to address the problem of handling trade-offs and to allow for cross-validation of the results. The employed fuzzy MCDA methods are as follows:
* TOPSIS (Technique for the Order of Prioritization by Similarity to Ideal Solution) [16]: This method ranks alternatives based on their distance from an ideal solution-best possible outcome and a negative-ideal solution-worst possible outcome. It is useful in the presence of many conflicting criteria to be optimized, such as processing power vs. power consumption. * MABAC (Multi-Attribute Border Approximation Area Comparison) [46]: This method is designed to rank alternatives based on their relative distance from the boundary of acceptability, and it is ideal for cases where the decision involves defining thresholds or boundaries for acceptable hardware performance under fuzzy conditions. * COMET (Characteristic Objects Method) [47]: This method evaluates decision alternatives according to their characteristic objects and ranks them according to a comprehensive comparison of criteria.