## 5 Experiments
### Test of the Proposed Method
#### 5.1.2 Application of the Method to the Scenarios
* Statistical Analysis: An average score for each criterion from the experts is calculated. To understand the level of agreement or disagreement among the experts, SD was calculated to understand how the scores varied around the mean. A low SD (<1) indicated strong agreement, while a high SD (>1) showed variability in expert opinions. Population standard deviation is used to find the SD: \(\sigma=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(x_{i}-u\right)^{2}}\) where N is the total number of experts, \(x_{i}\) individual expert score, and \(u\) is the mean (average) of all the scores. * Final Weight Assignment: After analyzing the statistical data, assigning final weights based on consensus was crucial. Since criteria with low SD reflected a strong consensus, their weights were directly based on the mean. In contrast, criteria with high SD (>1) required further discussion and adjustments of scores to ensure that the final weight reflected a more balanced perspective. The weight assignment session for edge hardware was finalized in two rounds of discussion. * Weight Derivation: Finally, weight derivation is employed through normalization, where mean scores were divided by the maximum score to produce normalized weights, and re-adjustment of the weights was performed so that they sum to 1, ensuring that they were correctly proportional. The weights of HW criteria for each scenario were finalized, as given in Appendix D, Table A6. For each scenario, hardware candidates are assessed based on the rankings calculated by the six fuzzy MCDA methods given in Section 2.6. The results of the evaluations for each scenario are provided in Appendix G (Fuzzy MCDA Software, Evaluation Results for Edge Hardware and AI Models). The best hardware options suggested by the majority of the methods for each scenario are provided in Table 4.  
According to the results in Table 4, the first ranking in all the fuzzy MCDA methods are the STM32H7 Series microcontrollers (EdgeHW_11) for Scenario I, NVIDIA Jetson Nano (EdgeHW_1) for Scenario II, and Raspberry Pi 4 for Scenario III. For Scenario 4, two hardware options are suggested: NVIDIA Jetson Xavier NX (EdgeHW_2) received the first ranking for the TOPSIS, COMET, and VIKOR methods, and NVIDIA Jetson AGX Orin (EdgeHW_3) obtained the first ranking for the MABAC and SPOTIS methods. In addition, to cross-validate the results of the case study and enable the comparison of the edge hardware products listed in Table 3, each hardware criterion is rated based on a relative comparison. For rating, a 1 to 5 scale is used, where 5 indicates the best performance \begin{table}
\begin{tabular}{l l l} \hline \hline
**Scenario** & **Edge Hardware ID** & **Suggested HW Option** \\ \hline Scenario I & EdgeHW\_11 & STM32H7 Series Microcontrollers \\ \hline Scenario II & EdgeHW\_1 & NVIDIA Jetson Nano \\ \hline Scenario III & EdgeHW\_6 & Raspberry Pi4 \\ \hline Scenario IV & EdgeHW\_2, EdgeHW\_3 & NVIDIA Xavier NX, Jetson AGX Orin \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Suggested edge HW option/s for scenarios. or suitability for the feature, and 1 indicates the least performance or suitability. The ratings of each metric of the hardware candidates are provided in Appendix E, Edge Hardware--Metrics Rating. Measurement Layer/Measurement for AI Model Process Flow--Step 8: Measurement Execution for AI Model Criteria (Collect Data for AI Model Metrics/Criteria)  
After determining the best edge hardware options for each scenario, the AI model candidates for the suggested hardware options are determined.