# A Geographical Heuristic Routing Protocol for VANETs
## 5 Performance Evaluation
### Evaluation of the Forwarding Phase in GHR
Hence, the use of different forwarding techniques (i.e., \(\alpha\rightarrow\infty\), \(\alpha=0\), \(\alpha=3\) or _First_) produces a statistically-significant change among them in all performance metrics, and therefore, they require pairwise comparisons to analyze the differences. These pairwise comparisons are not needed for the average delay metric in all densities and for the percentage of packet losses in the medium density scenario, because there are no differences among the forwarding techniques in these cases. Following Step 3 of the statistical analysis, Table 4 shows the results of the pairwise comparisons among the forwarding techniques (i.e., (\(\alpha\rightarrow\infty\), \(\alpha=3\)), (\(\alpha\rightarrow\infty\), \(\alpha=0\)), (\(\alpha=3\), \(\alpha=0\)), (\(\alpha=3\), \(First\)) and (\(\alpha=0\), \(First\))) in which there is no statistical significance for a particular metric (i.e., \(p\)-value \(\geq\) 0.05). In these cases, the average values of the metrics are very similar and can be considered statistically the same. The rest of the results of the pairwise comparison tests (not included in Table 4), e.g., the percentage of packet losses for the high density scenario when Tabu is not enabled, obtained \(p\)-values \(<\) 0.05. Those \(p\)-values indicate that the forwarding technique (i.e., best node, SA, random or first) has an impact on the values of the metrics. The comparisons of average values of the four performance metrics with the different forwarding techniques are depicted in Figure 4. Considering the previous statistical analysis, we continue analyzing the behavior of forwarding techniques in the metrics. Firstly, the use of our Tabu list, which consists of the three last nodes in the packet path, decreases the percentage of packet losses considerably. This descent in the packet losses goes from 6% in low density scenario till around 10% with high vehicle density, as can be seen in Figure 4a. The reason for this improvement is that our Tabu list provides memory to the routing decision. This memory helps to avoid neighbors of the current node already visited by the packet that otherwise could be selected again to forward the packet. Therefore, the Tabu list avoids loops and helps to consider other possible next forwarding nodes. On the other hand, the use of our Tabu routing proposal increases the average number of hops (see Figure 4c) by around 0.6 hops on average. Forbidding nodes as next hops forces nodes to search other path that might be longer. Moreover, the average end-to-end delay increases around 2 s for the low vehicle density scenario and 1.5 s for the high density \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Number of Density** & **Tabu** & **Wilk’s A** & **F (3,17)** & \(p\)**-Value** \\ \hline \multirow{4}{*}{\% of Packet Losses} & 100 & together & 0.516 & 5.306 & 0.009 \\  & **150** & **together** & **0.666** & **2.846** & **0.068** \\  & 250 & No & 0.237 & 18.222 & 0.0005 \\  & 250 & Yes & 0.456 & 6.747 & 0.003 \\ \hline \multirow{4}{*}{Average delay} & **100** & **together** & **0.894** & **0.671** & **0.582** \\  & **150** & **together** & **0.975** & **0.145** & **0.932** \\  & **250** & **together** & **0.662** & **2.890** & **0.066** \\ \hline \multirow{4}{*}{Average number of hops} & 100 & together & 0.163 & 29.14 & 0.0005 \\  & 150 & No & 0.198 & 22.901 & 0.0005 \\  & 150 & Yes & 0.081 & 63.96 & 0.0005 \\  & 250 & No & 0.046 & 118.0 & 0.0005 \\  & 250 & Yes & 0.022 & 253.14 & 0.0005 \\ \hline \multirow{4}{*}{\% of idle time} & 100 & together & 0.332 & 69.079 & 0.0005 \\  & 150 & together & 0.203 & 22.22 & 0.0005 \\ \cline{1-1}  & 250 & No & 0.137 & 35.77 & 0.0005 \\ \cline{1-1}  & 250 & Yes & 0.155 & 30.979 & 0.0005 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MANOVA [37] results of the testing difference in performance metrics among routing forwarding techniques (_FT_). There is a significant difference when the \(p\)-value \(<\) 0.05.