# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 4 Evaluation
### Simulation Results  
Figure 3 shows the number of alive UAVs for both SD-UAVNet and VOEI in a scenario that UAVs are fly with different speed limits. By analyzing the results of Figure 3a, we can conclude that the first UAV to finish its energy resource occurs at the simulation time of 140 s for UAVs considering SD-UAVNet and flying with speeds ranging from 1 to 5 m/s. During this simulation time, such UAV was selected as the source node \(SN\) to capture the video and as the relay node \(RN_{i}\), as well as it flew with speed closer to the maximum speed limits (i.e., 5 m/s) most of the time. Hence, such UAV consumed more energy compared to other UAVs, leading it to finish its energy resources at the simulation time of 140 s. In addition, the simulation finished with about 50% of the initial number of UAVs by considering the SD-UAVNet with UAVs flying with speeds ranging from 1 to 5 m/s. On the other hand, the simulation finished with almost of all UAVs by considering VOEI for UAVs flying with speeds ranging from 1 to 5 m/s. This is because VOEI establishes energy-efficiency routes and prevents UAV loss caused by energy issue. For instance, VOEI detects the appropriate moment for UAV replacement of UAVs with lower energy resources. Hence, VOEI provides energy-efficiency operations, avoiding network death, route failure, and void area, as well as network partitioning compared to SD-UAVNet. As mentioned before, UAV movements require much more energy than for packet transmissions. In this way, as soon as the speed limits increase, the time that the first UAV die reduces, as well as the time that all UAVs finishes the energy resources for the scenario with SD-UAVNet. This can be seen by comparing the results of Figure 3a-d. For instance, the simulation time that the first UAV finishes its energy source reduced from 140 s for UAVs flying with lower speed (i.e., 1 to 5 m/s) to 70 s for UAVs flying with higher speed limits (i.e., 15 to 20 m/s) by considering SD-UAVNet. In addition, all UAVs finished the energy resources at the simulation time of 140 s for UAVs flying with speed ranging from 15 to 20 m/s, which does not happen for UAVs flying with lower speed limits (i.e., 1 to 5 m/s) for the scenario with SD-UAVNet. It is also important to mention that some UAVs (i.e., 1-3) finished the energy resources during the simulation with VOEI, regardless of the flying speed. This is because such UAVs must wait for the source or relay node replacement before starting the UAV replacement task, which avoids route failure during video transmission (i.e., without interruption of the video services). This is because the main goal is to disseminate video in cases of an event and not only save energy. Figure 4 shows the remaining energy for UAVs flying with different speed limits for both SD-UAVNet and VOEI. By analyzing the results of Figure 4, we can conclude that the remaining energy of UAVs considering SD-UAVNet linearly reduces during the simulation time, regardless of the flying speed. This is because it does not consider UAV replacement operations. On the other hand, the remaining energy of UAVs considering VOEI decreases and increases during the simulation  
Figure 3: Number of live UAVs over time for UAVs flying with different speed limits. (**a**) UAVs flying with speed ranging from 1 to 5 m/s; (**b**) UAVs flying with speed ranging from 5 to 10 m/s; (**c**) UAVs flying with speed ranging from 10 to 15 m/s; (**d**) UAVs flying with speed ranging from 15 to 20 m/s. time, since VOEI analyzes all the time if each UAV has enough energy to fly back to the control center for battery replacement. Hence, as soon as the remaining energy \(RE_{i}\) of a given UAV is closer to the required energy for battery replacement, such UAV must perform the UAV replacement operations in order to prevent UAV loss caused by energy issue, which will cause video delivery failure.