# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 5 Evaluation of OLSR-HBP in Mobile Scenarios
### Impact of OLSR-HBP on the Network Performance
#### 5.4.1 Data Transfer Capability  
To confirm that OLSR-HBP does not negatively affect the data transmission through the network, we performed some additional tests. To simulate data traffic, we used periodic ping requests with 1 Kbyte payload each. We divided the network nodes into two groups. The first group sent periodic ping requests to one node in the second group (every pair of nodes transferred pings between them). The ping periods used were 10, 1 and 0.1 s. The analysed metrics were the message delivery rate (percentage of ping requests returned to the sender) and round-trip time (RTT--elapsed time between the instant when the request is sent and when the response is received), which corresponds to the latency. We compared the metric values between the original OLSR (where all TP messages are sent) and OLSR-HBP (where some TP messages were not sent, because they can be predicted at destination). Table 7 shows the percentages of ping delivery (i.e., the number of pings received divided by number of pings sent). We consider the same four test cases of Section 5.3.1 for both, OLSR and OLSR-HBP. The first row represents the baseline values and corresponds to the delivery percentage of OLSR (\(DP_{OLSR}\)). The second row contains the delivery percentage (\(DP_{OLSR-HBP}\)) of the implemented predictor. Finally, the third row indicates the improvement achieved that is computed as \(Improvement=(DP_{OLSR-HBP}-DP_{OLSR})/DP_{OLSR}\). As a result, positive improvement values mean that OLSR-HBP achieves higher delivery percentage, whereas negative improvement values mean that the predictor performs worse than the original OLSR. We can also observe that OLSR-HBP performs similar to OLSR, with a maximum of \(-5.9\%\) reduction in the delivery rate. This performance is reached when using a SLAW mobility pattern, with the highest number of nodes, largest execution time and highest ping frequency. On the other hand, for the static/grid case, OLSR-HBP keeps the maximum delivery rate for a 10 s ping period. For a 1 s ping period, OLSR-HBP improves the delivery rate by 3.38%, and for a 0.1 s period its delivery rate is 7.23% lower than the regular OLSR protocol. The average improvement value is \(-2.28\%\) (almost negligible), therefore, we can conclude that OLSR-HBP reaches and keeps the delivery rates of the regular OLSR protocol (baseline). The second metric that we analysed was the average RTT (round-trip time) of the pings. Table 8 shows the RTT times (in milliseconds) for the four cases considered, and the percentage of improvement achieved. We denote as \(RTT_{OLSR}\) and \(RTT_{OLSR-HBP}\) to the RTT values for OLSR and OLSR-HBP respectively; then we define the RTT improvement as \(RTT_{Improvement}=(RTT_{OLSR}-RTT_{OLSR-HBP})/RTT_{OLSR}\). Due to the fact that the RTT is best for small values, \(RTT_{Improvement}\) is positive when \(RTT_{OLSR-HBP}\) is smaller than \(RTT_{OLSR}\), and negative otherwise. The columns with ping periods of 0.1 s and execution times of 4 h could not be calculated given that the average RTT computation uses 16 bit sequences (icmp_seq), and at a rate of 0.1 s for 4 h, the number of pings overflows the 16 bit capacity. This means that the RTT computation is wrong.