# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 5 Performance Evaluation and Results
### Delivery Rate  
Figure 7 depicts the delivery ratio simulation results of the various protocols as a function of the number of nodes deployed in the network.  
The delivery rate for most protocols increases as the network density (i.e., the number of nodes) increases, as more contacts between nodes help message dissemination. One exception is the GeoEpidemic protocol that disseminates messages without any geographical constraints, thus wasting resources outside the ROI. This means that nodes can be congested with messages that are not useful, because they are outside their ROI, preventing other useful messages from being disseminated. This is particularly significant for geocast groups with small ROI. Therefore, the GeoEpidemic delivery rate does not increase for cases with more than 160 nodes. The other protocols limit message dissemination outside the ROI, so they are less affected by congestion. The V-GRADIENT always achieves the highest delivery ratio compared with the other protocols. using the case of the 160 nodes as an example, the V-GRADIENT shows a 19.5% increase in the overall delivery rate in relation to the GeoS&W protocol,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Message Event Generator** & **Generator 1** & **Generator 2** & **Generator 3** \\ \hline Message size interval & 500 kB–1 MB & 500 kB–1 MB & 500 kB–1 MB \\ Message generation interval & 60–120 s & 80–140 s & 100–160 s \\ Messages’ initial TTL & 100 min & 60 min & 80 min \\ ROI radius & 600 m & 400 m & 300 m \\ Geocast group identifier & ID = 1 & ID = 2 & ID = 3 \\ Geocast group penetration rate & 100\% & 50\% & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for the distinct message event generators used in the Helsinki scenario.  
Figure 6: Simulation map of downtown Helsinki. The streets are marked gray. The map scale is shown at the top. Nodes are numbered in blue. Regions of interest marked with red, blue, and black for geocast groups one, two, and three. Green circles depict the nodes’ communication ranges.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group of Nodes** & **Group Size** & \multicolumn{3}{c}{**Interested in Messages with Geocast Group IDs:**} \\  & & **ID = 1** & **ID = 2** & **ID = 3** \\ \hline Group 1 & 25\% of vehicles & ✓ & ✗ & ✗ \\ Group 2 & 50\% of vehicles & ✓ & ✓ & ✗ \\ Group 3 & 25\% of vehicles & ✓ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Groups of vehicles interested in the distinct geocast services for the Helsinki scenario.  
40.4% in relation to the MC-GeoDirectDelivery, 56.4% in relation to the GeoEpidemic Constrained, and 84.1% in relation to the GeoEpidemic protocol.  
The GeoEpidemic Constrained protocol has very poor performance for sparse networks (40 nodes), as it drops messages as soon as a node carrying them leaves the ROI. If there are very few nodes in the ROI and they leave it, messages will be permanently deleted, resulting in low delivery rates for sparse networks. For dense networks, flooding messages within the ROI is efficient and deleting them outside the ROI reduces network congestion, resulting in an improvement in the delivery rate.  
MC-GeoDirectDelivery retains messages as nodes leave the ROI. This results in better performance for sparse networks, as more message copies circulate in the network, but degrades the performance for dense networks, as the resulting congestion reduces the delivery efficiency outside the ROI.  
GeoS&W creates a limited number of message copies in nodes that are not direct destinations. This limits the congestion and also helps to spread messages in sparse scenarios, resulting in the second-best performance.  
The V-GRADIENT has the best delivery ratio results proving the benefit of using a dynamic dropping policy that is stricter in denser networks to avoid congestion and softer in sparse networks to improve message dissemination. In addition, the more intelligent spraying of messages is more efficient than in GeoS&W.