## 6 Matching User Preferences to Network Response
### Combining Evaluation Metrics: An Example  
To further illustrate the scoring process involved when choosing a suitable communications solution an example is provided. This example demonstrates the criteria scoring, scaling and weighting involved in decision making using data and models presented in Section 5.1. First, a hypothetical set of physical attributes for the network are defined (Table 4). Using the models an expected value for each of the evaluation metrics for all solutions is determined. After applying both the scoring and scaling structure a score for each criteria is available for preference weighting. These scores are shown in Table 5. As latency, efficiency and stability are minimal metrics, a positive score is required for values lower than the mean and visa versa. Equation (2) is adapted to facilitate minimal metrics giving Equation (3). The imbalance due to the extended value ranges for both efficiency and stability are corrected by adding a scaling weight of 0.5 to the score. The scaling scheme used for the reliability criterion does not effect any of the results in this example. \begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Feature** & **Value** \\ \hline Density & 21 \\ Scale & 3 \\ Size & 137 \\ Density (SD) & 6 \\ Scale (SD) & 1 \\ Channel Quality & light \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Physical attributes of the network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solution** & **Criteria** & **Value** & **Criteria Score** & **Scaling Factor** & **Overall Score** \\ \hline \multirow{4}{*}{ETX-NH} & Reliability & 96.29 & 2 & 1 & 2 \\  & Latency & 0.125 & \(-\)12 & N/A & \(-\)12 \\  & Efficiency & 19,570 & 7 & 0.5 & 3.5 \\  & Stability & 216.7 & 29 & 0.5 & 14.5 \\ \hline \multirow{4}{*}{ETX} & Reliability & 96.44 & 2 & 1 & 2 \\  & Latency & 0.113 & \(-\)2 & N/A & \(-\)2 \\  & Efficiency & 18,520 & 13 & 0.5 & 6.5 \\  & Stability & 299.5 & 2 & 0.5 & 1 \\ \hline \multirow{4}{*}{ETT} & Reliability & 90.86 & \(-\)4 & 1 & \(-\)4 \\  & Latency & 0.096 & 14 & N/A & 14 \\  & Efficiency & 25,520 & \(-\)20 & 0.5 & \(-\)10 \\  & Stability & 397.7 & \(-\)31 & 0.5 & \(-\)15.5 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} mean values: Reliability = \(\frac{96.44+96.29+03.66}{3}\) = \(\frac{94.53}{3}\); Latency = \(\frac{91.13+0.125+0.066}{3}\) = \(\frac{18520+19750+25520}{3}\); Stability = \(\frac{299.52+126.73+397.69}{3}\) = \(304.64\). \\ \end{tabular} } & \multirow{4}{*}{\(=\) 0.111;} \\  & & & & \\ \end{tabular}
\end{table} 
Table 5: Scoring the criteria for each solution. Table 5 shows the criteria scores before a weighting is applied. The weighting is applied in direct correlation to the application preferences. After the weighting is applied the scores can be added to determine a winner. A different winner can be determined depending on the preference of the application. Tables 6-8 show a number of preference models and winner outcomes of each. The preferences are detailed in Table 9.  
The preferences are used as a multiplicative weighting factor on the criteria scores. After weighting the criteria each of the criteria scores are summed to create the solution score. Table 6 shows the solution scores which advise best solution for the preference set A. This preference set holds all criteria at equal importance. The ETX-NH solution is a marginal winner with this preference model due to a strong performance for reliability and stability. ETX is the chosen solution for preference set B. Increased importance is placed on both reliability and latency with little importance placed on stability and efficiency. Despite the increased importance focusing on latency in preference set C, ETX is chosen as the best solution. This is due to the poor performance shown by the ETT solution in both stability and efficiency.