# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 5 Experiments and Results
### Dependence of the TARA Method Performance on the Delay Function
#### 5.1.2 Linear Delay Function  
Result plots for the Linear Delay function have similar shapes compared to the Constant Delay function, compare Figures 8 and 9 with Figures 6 and 7. However, with the same configuration parameters, the TARA method with the Linear Delay function achieves significantly better \(PDR_{avg}\) and \(RDD_{avg}\). For the Linear Delay function, smaller \(MD\) values penalize all nodes almost the same way, and penalization became fairer with greater values. \(THR\) value does not influence \(PDR_{avg}\) significantly. That is because the linearity of the delay function already plays the same role as the \(THR\) in Constant Delay function. Further dependence found in results is that the \(RDD_{avg}\) depends on \(MD\) of the Linear Delay function. Increasing \(MD\) prolongs the path length and increases the route discovery time. \(RDD\)s for the Linear Delay function are better than for the Constant Delay one. The explanation is that by penalizing worse nodes more, the Linear Delay function generally delays RREQ messages less in comparison to the Constant Delay function. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Flag**} & **Perfor.** & **Best** & \multirow{2}{*}{\(\delta_{metric}\)} & \multicolumn{2}{c}{**Parameters**} & \multicolumn{3}{c}{**Other Metric Values for Context**} \\  & **Metrics** & **Value** & & \(THR\) & \(MD\) & \(PDR_{avg}\) & \(PL_{avg}\) & \(RDD_{avg}\) & \(RDD_{max}\) \\ \hline \multirow{2}{*}{DFT} & \(PDR_{avg}\) & 0.53 & 51\% & 0.7 & 16 & - & 7.60 & 17.90 & 102 \\  & \(RDD_{avg}\) & 7.22 & 18\% & 0.6 & 1 & 0.45 & 6.37 & - & 19 \\ \hline \multirow{2}{*}{DFF} & \(PDR_{avg}\) & 0.52 & 54\% & 0.6 & 16 & - & 8.57 & 2.82 & 63 \\  & \(RDD_{avg}\) & 1.57 & 11\% & 0.6 & 1 & 0.43 & 7.70 & - & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Constant Delay function parameters that provided the best values of metrics. Figure 7: Constant Delay function + DFF. The best metric values and combinations of the Linear Delay function parameters to reach them are presented in Table 4.  
Table 5 presents the combinations of parameters for the Linear Delay function to find the best trade-off between contradictory requirements to maximize \(PDR_{avg}\) and minimize \(RDD_{avg}\). Clearly, large \(MD\) increases \(RDD_{avg}\) significantly while \(PDR_{avg}\) improves very little. This table can be used to find the best configuration parameters once the real-world application gives us the relative importance of maximizing \(PDR_{avg}\) vs minimizing \(RDD_{avg}\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Flag**} & **Perfor.** & **Best** & \multirow{2}{*}{\(\delta_{metric}\)} & \multicolumn{3}{c}{**Parameters**} & \multicolumn{3}{c}{**Other Metric Values for Context**} \\  & **Metrics** & **Value** & & & \(THR\) & \(MD\) & \(PDR_{avg}\) & \(PL_{avg}\) & \(RDD_{avg}\) & \(RDD_{max}\) \\ \hline DFT & \(\begin{matrix}PDR_{avg}\\ RDD_{avg}\end{matrix}\) & 0.60 & 71\% & 1 & 512 & - & 8.64 & 273.75 & 1626 \\ \hline DFT & \(\begin{matrix}PDR_{avg}\\ RDD_{avg}\end{matrix}\) & 6.79 & 11\% & 0.5 & 1 & 0.40 & 6.17 & - & 19 \\ \hline DFT & \(\begin{matrix}PDR_{avg}\\ RDD_{avg}\end{matrix}\) & 0.58 & 72\% & 1 & 512 & - & 9.56 & 49.05 & 1129 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Linear Delay function parameters that provided the best values of metrics. Figure 8: Linear Delay function + DFT.