# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 5 Experimental Results
### RQ3: Which Mobility Model Stows the Highest PDR Performance in the Reconnaissance Scenario?
In Figure 5d, the SRWP model shows the highest Reconnaissance PDR performance (42%) up to when the number of nodes is 20. After that, the RDPZ model shows the highest Reconnaissance PDR performance (40%). EGM also shows a reasonable Reconnaissance PDR performance (33%). In contrast, DPR and MP show relatively low Reconnaissance PDR performance (19% and 24%, respectively). Figure 5f shows the Reconnaissance PDR performance over 60 min. The highest performance is 76% with SRWP when the number of nodes is 15. The second highest performance is 65% with MP when the number of nodes is 20. The performance of RDPZ is 62% when the number of nodes is 20. The performance of EGM is 59% when the number of nodes is 15. The lowest performance is 41% with DPR when the number of nodes is 20. The average PDR performance is 61% for SRWP, 50% for RDPZ, 49% for EGM, 48% for MP, and 30% for DPR. We compare the results for RQ4 with the results for RQ3. The results for RQ3 showed good performance in the order of SRWP, MP, RDPZ, EGM, and DPR. The results for RQ4 showed good performance in the order of SRWP, RDPZ, EGM, MP, and DPR. From the comparison, we can see that SRWP reasonably performs in terms of PDR and a reconnaissance ratio. RDPZ model with excellent reconnaissance rate could be suitable for communication between GCS and nodes, as well. In contrast, MP showed high PDR performance. However, when a reconnaissance ratio was considered, MP was not appropriate for our reconnaissance scenario. The reason for this is that we observed that the MP model moves only near the GCS (center of the reconnaissance area) in our simulation. **Findings. ** If we consider both the reconnaissance rate of a mobility model and the PDR performance of a mobility model, the mobility models suitable for protocol communication in the reconnaissance scenario is SRWP. Alternatively, RDPZ and EGM can be used for our reconnaissance scenario. RQ5: How Does the GCS Location Affect the Performance of the Mobility Model in the Reconnaissance Scenario? Figure 6 shows the results of the performance analysis according to the change in the GCS location. In the experiment, we used the AODV routing protocol, which has shown good performance, and we analyzed the performance changes in five mobility models. We set the starting position of the UAV at the bottom. Even if the position of the GCS changes, the starting position of the UAV remains the same. Figure 6a shows the position of the GCS in the simulation area, Figure 6b shows the PDR when the GCS is located at the left top, Figure 6c shows the PDR when the GCS is located at the top, Figure 6d shows the PDR when the GCS is located at the middle, and Figure 6e shows the PDR when the GCR is located at the bottom. When the GCS is located in the middle of the reconnaissance area, the mobility models yield high PDR values overall. The PDR values become lower when the GCS is located at the bottom, at the top, and at the left top. Additionally, the five mobility models show different trends across the different GCS locations. Figure 5: Reconnaissance packet delivery ratio: (**a**) reconnaissance rate by the mobility model for 10 min, (**b**) reconnaissance rate by the mobility model for 30 min, (**c**) reconnaissance rate by the mobility model for 60 min, (**d**) RPDR for 10 min, (**e**) RPDR for 30 min, and (**f**) RPDR for 60 min. At each different GCS location, a different mobility model shows the highest PDR value. When the GCS is located at the left top, RDPZ shows the highest PRD value. When the GCS is located at the top or in the middle, SWRP shows the highest PRD value. When the GCS is located at the bottom, MP shows a higher PDR value. The mobility models show different performances according to different GCS locations due to the different mechanisms of the mobility models. For example, in the SRWP model, a UAV moves in a straight line by setting an arbitrary destination from the current location, and this method often passes through the middle. In the MP model, a UAV often hovers at the bottom.