# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 4 Implementation of the OLSR-HBP
### Analysis of the OLSR-HBP Implementation  
The implementation of OLSR-HBP was validated using stationary network scenarios, since it allows us to be sure that the predictor is working as expected. The obtained results were analyzed using various network metrics. In these interaction scenarios we also compared the impact of using OLSR-HBP and the standard OLSR routing protocol. Results from this comparison establish a benchmark that explains the performance of OLSR-HBP in mobile interaction scenarios, which is the target of this proposal. The simulation scenario considered a 4 \(\times\) 4 wireless mesh backbone network, where edge routers generate packets for other edge routers, and inner routers just forward packets. This scenario is similar to the one described in [11]. Aligned with such an experiment we also considered 1 KByte ping data packets, sent every 1 and 10 seconds, which corresponds to a range from 1.2 to 12.2 Mbps of data traffic. The setting details are specified in Table 2.  
With the NS-3 WiFi configuration, the coverage range of the nodes is approximately 70 m. It is due to the transmission gain (dB) and the energy threshold (dbm) required to allow the PHY layer of NS-3 to detect the signal. The simulations used the default configuration of the OLSR protocol [35]. This configuration considers the delivery of HELLO messages every two seconds and TP messages every five seconds. Table 3 summarizes the main results of this study. In the considered static scenario, OLSR-HBP outperforms the OLSR routing protocol in several performance metrics. Moreover, the number of TP messages that can be correctly predicted (and thus not transmitted) achieves very high values (more than 99%), which indicates that the implementation of the predictor is working as expected. In order to make a more in depth validation of the OLSR-HBP performance, and considering OLSR as ground truth, we compared both protocols considering the following performance metrics: precision, recall, F1 score, and area under the curve (AUC). Precision is the number of true positives (TrP) divided by number of TrP and false positives (FaP); thus precision is defined as follows: \(P=TrP/(TrP+FaP)\). Recall is the number of TrP divided by the number of TrP and false negatives (FaN); it means that \(R=TrP/(TrP+FaN)\). The F1 score is the harmonic mean of Precision and Recall. Finally, the AUC score is calculated by plotting the Recall against the false positive rate at various threshold settings. As OLSR-HBP is a multiclass predictor (it has as many classes as TC identifiers), we assumed the _micro_ and _macro_ averaging approaches to aggregate the metric scores of each class. Meanwhile the first approach considers the global counting of the total true positives, the second one calculates the metric for each class and find their unweighted mean. Table 4 shows the performance results for the static scenario considering different packet generation periods. As expected, these metric results confirm that the proposed predictor is working properly. Therefore, the next step is to evaluate its performance in mobile scenarios to determine the percentage of message reduction that can be expected when it is used. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Packet Generation Period** & **10 s** & **1 s** \\ \hline Precision (micro) & 0.999 & 0.999 \\ Precision (macro) & 0.999 & 0.999 \\ Recall (micro) & 0.999 & 0.999 \\ F1 (micro) & 0.999 & 0.999 \\ F1 (macro) & 0.999 & 0.999 \\ AUC (micro) & 0.999 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the performance metrics in static scenarios, using OLSR as ground truth.