# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 5 Experiments and Results
### Summary Evaluation of Experiment Results  
Results of experiments for all delay functions show that increasing maximum delay value \(MD\) improves network-wide average \(PDR\) significantly, but the effect weakens with greater values. Moreover, the initial increase in \(MD\) gives a quick boost for the \(PDR\) metric, but a further increase in \(MD\) is not effective. Improvement of \(PDR\) by the TARA method, compared to the reference experiment with no delay function applied, is significant for all delay functions, but Exponential Delay provided the best improvement.  
Figure 14: Paths created with different _destination only_ flag setting.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Flag** & **Scen.** & \(PDR_{avg}\) & **Ref.\(PDR_{avg}\)** & \(\delta_{PDR_{avg}}\) & \(RDD_{avg}\) & **Ref.\(RDD_{avg}\)** & \(\delta_{RDD_{avg}}\) \\ \hline \multirow{6}{*}{DFT} & SC-10 & 0.99 & 0.83 & 19\% & 10.71 & 6.13 & 75\% \\  & SC-20 & 0.95 & 0.69 & 39\% & 23.08 & 6.13 & 276\% \\  & SC-30 & 0.89 & 0.55 & 63\% & 39.77 & 6.13 & 549\% \\  & SC-40 & 0.75 & 0.44 & 71\% & 84.63 & 6.13 & 1281\% \\  & SC-50 & 0.61 & 0.35 & 72\% & 145.93 & 6.13 & 2281\% \\ \hline \multirow{6}{*}{DFF} & SC-10 & 0.99 & 0.80 & 24\% & 2.56 & 1.42 & 80\% \\  & SC-20 & 0.95 & 0.64 & 49\% & 4.54 & 1.42 & 220\% \\ \cline{1-1}  & SC-30 & 0.89 & 0.50 & 76\% & 8.92 & 1.42 & 527\% \\ \cline{1-1}  & SC-40 & 0.74 & 0.42 & 78\% & 16.79 & 1.42 & 1081\% \\ \cline{1-1}  & SC-50 & 0.59 & 0.34 & 74\% & 26.74 & 1.42 & 1781\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Different trust distribution scenarios.  
_THR_ values do not influence \(PDR_{avg}\) as significantly as the _MD_ values do. For the Linear Delay function, _THR_ has influence mainly on the \(RDD_{avg}\). The reason is that nodes with \(PDR\) above _THR_ are not penalized, so no delay is added for them. Thus, route discovery, in general, is faster.  
If the Exponential Delay function is compared with the Linear Delay function, Exponential Delay penalization better reflects the differences in trust values of nodes. Depending on the bending of the exponential curve, penalization of nodes with high \(PDR\) starts slowly, which is sufficient, and penalization of nodes with low \(PDR\) value grows rapidly.  
\(PL_{avg}\) and \(RDD_{avg}\) metrics need to be minimized, contrary to the average \(PDR_{avg}\), which needs to be maximized. Due to the delay function, improving the \(PDR\) means worsening \(PL\) and \(RDD\). Greater \(MD\) value prolongs the path length and increases the route discovery time.  
In general, applying the TARA method approach in more dense networks gives greater \(PDR_{avg}\) improvement. At the same time, the network size, under the assumption of the same density, has no influence on the performance of the TARA method.  
DFT provides more trusted and shorter paths for the cost of much greater \(RDD_{avg}\). DFF copes better with discovering paths quickly but produces longer paths and cannot reach so high \(PDR_{avg}\) as DFT.  
Experiments with different scenarios showed that networks with a greater amount of untrusted nodes have a greater potential for improvement.  
The aim of the experiments was to find the best combination of TARA method parameters to achieve two contradictory goals, which should also be kept in mind when applying the TARA method:  
* maximize the average packet delivery ratio in the network \(PDR_{avg}\);
* minimize the average route discovery delay \(RDD_{avg}\).  
Combinations of delay functions parameters that produce relatively high \(PDR_{avg}\) while \(RDD_{avg}\) is low are presented in Tables 5 and 7.