## 5 Results and Analysis
### The Effect of UAV Speed  
To achieve the desired impact of UAV speed during the SAR mission, the speed of the UAVs can be varied. To explore the behaviors of the four distinct routing protocols in the high-speed environment, the number of UAVs has been kept constant at 50 throughout the experiment. However, their speeds have been increased to 10, 20, 30, 40, and 50 m per second (m/s). The simulation results (shown in Figure 6) show that the ML-OLSR achieves the highest PDR when compared to other protocols at various UAV speeds. This is because ML-OLSR chooses reliable UAVs to act as MPRs in order to transmit data packets and control messages. On the other hand, standard OLSR demonstrates a subpar performance concerning PDR. P-OLSR and D-OLSR both deliver performance marginally superior to that of OLSR. Additionally, we may have seen that the PDR metrics for all routing protocols tend to fall when the speed of UAVs grows. The reason for this fact is that when the speed of UAVs increases, the network topology also changes. This results in an increased number of packets being lost. Figure 7 shows a comparison of the four different routing protocols' performances concerning latency. The standard OLSR protocols use short path algorithms with a minimum hop to deliver packets to the destination. However, this is not guaranteed to provide a minimal delay in UAV ad hoc networks because some links are broken or lost. This demonstrates that the shortest path metric in a UAV ad hoc network is not necessarily the optimal route metric to identify the optimum path when looking for the most efficient route. Figure 6: PDR vs. speed of UAVs. As there is a lower frequency of connection failures and route maintenance, ML-OLSR and P-OLSR both have lower overall latency. As shown in Figure 7, the amount of latency increases as the speed of the UAVs increases. This happens because when the UAV's speed increases, the network topology changes more often, resulting in a higher rate of link disconnection and a longer route-finding phase. It is essential for the success of search and rescue operations that data packets be delivered with as little delay as possible. The use of energy is the third performance metric that we considered. This is an important measurement for UAV ad hoc networks since these networks have limited energy sources. When the UAV speed increases, the network topology changes in high dynamicity. This leads to link failures or network partitions. Thus, UAVs need to exchange more messages for updating network status or sending messages for maintaining or discovering routes. Therefore, more energy is required to transmit these messages. Figure 8 shows the amount of energy consumption that the OLSR, D-OLSR, ML-OLSR, and P-OLSR all utilize. The simulation results reveal that an increase in UAV speed increases the amount of energy consumed by the four OSLR-based routing protocols. This happens because proactive protocols exchange messages periodically to ensure that the routing table is kept up-to-date and that more messages are transmitted to maintain or discover the route. The routing table is constantly accessible. In addition to this, it was discovered that ML-OLSR is superior to other protocols in terms of its ability to conserve energy. The D-OLSR protocol offers improved energy efficiency compared to the OLSR protocol. This occurs as a result of the fact that it optimizes the number of MPRs even when the distance between UAVs increases relatively. Finally, throughput may be defined as the successful reception of a data packet at its destination and is expressed in kbps. Figure 9 presents a comparison of the throughput metrics that are used by the four different routing techniques. If the UAV's speed is increased, the throughput value will fall since the link disconnection chance will also rise, which will result in more packets being lost.