# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 5 Evaluation of OLSR-HBP in Mobile Scenarios
### Performance of OLSR-HBP
#### 5.3.1 Control Traffic Decrease  
Decreasing the signalling overhead (number of TP messages sent through the network) is an important goal for this proposal. As explained before, the routing protocol that is used can avoid delivering a TP broadcast when all the destination nodes are able to correctly predict it (i.e., it is a _global hit_). If only one of the destination nodes is unable to predict the TP message, it has to be broadcast. This requirement of a global hit will produce results that are worse than those presented in Section 4, where the hit was considered just at the origin node.  
Figure 5 plots the number of TP broadcasts for four different cases. The values shown in this figure were normalized, representing the average number of TP broadcasts per node and per hour. The values depicted in blue represent the number of broadcasts that are actually sent through the network. The broadcasts in green correspond to global hits, and therefore, to TP messages that are not transmitted (they can be correctly predicted at the destination nodes). The four cases used the SLAW mobility pattern considering 10 or 20 nodes, and 1 or 4 h of execution. The results show lower percentages of skipped TP broadcasts (between 40% and 54%). Although the values are normalized, we can observe that the cases with 20 nodes include more broadcasts because of both, the possible combinations of nodes, and the number of MPR nodes that broadcasts TP messages is higher.  
If we compare the results shown in Figure 5 with those of the predictability limits reported in [12], the percentages of 70-85% of predictability limits for SLAW (considering 10, 20 nodes and 1, 4 h), decrease to 40-54% when using OLSR-HBP. This can be explained due to the fact that not all hits at origin nodes are global hits. Another explanation for this difference is that in Figure 5, the prediction percentages increased with larger historical-data windows, but OLSR-HBP performs better with shorter historical-data windows, as shown in [12]. This means that for this predictor it is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Packet Generation Period** & **-** & **10 s** & **1 s** & **0.1 s** \\ \hline Precision (micro) & 0.767–0.897 & 0.768–0.897 & 0.767–0.897 & 0.767–0.899 \\ Precision (macro) & 0.719–0.842 & 0.719–0.842 & 0.717–0.839 & 0.715–0.851 \\ Recall (micro) & 0.767–0.897 &  
more beneficial to work with shorter historical-data windows (using 1 h is better than 4 h). To take advantage of it, the predictor could delete the historical data every hour.  
Figure 6 shows the results from the perspective of the receiver of the TP messages. The absolute number of messages at the destination is greater than at origin (Figure 5) because every single broadcast message can be received by several nodes (we accumulate every TP message received at each node). The trends observed in Figure 5 are repeated now, but with higher percentages of predictable TP messages at the destination. In fact, SLAW with 10 nodes and 1 h historical-data window achieves 85.15% of prediction success. For the other cases, with the SLAW mobility pattern, the prediction percentage decreases with a higher number of nodes and larger historical-data windows. Particularly, it tends to decrease, reaching 82.83% (with 10 nodes and 4 h), 76.20% (with 20 nodes and 1 h) and 71.32% (with 20 nodes and 4 h).  
Figure 5: Average number of topology propagation (TP) broadcasts (per node, per hour).  
Figure 6: Average and normalized number of TP messages received/predicted at destination nodes.