# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 8 Simulation Results and Discussion
### Performance Evaluation
#### 8.3.2 Coverage Performance  
Figures 13-15 show the coverage performance of each scheme for three targets, as measured by coverage vs. time, total coverage, and fairness. We again evaluate the UAV network at two node densities (30 and 50 UAVs) and speeds (20 and 40 m/s). The performance of AODV is identical to that of Pipe, since both use the same BS-CAP mobility model.  
Figure 12: Routing Performance: Route Breaks for three-target settings C\({}_{4}\), C\({}_{5}\) and C\({}_{6}\).  
Figure 13: Coverage vs. Time plots for three-target settings C\({}_{4}\), C\({}_{5}\) and C\({}_{6}\).  
For the BS-CAP mobility model, we set \(\beta=1.5\) and set \(\omega=0.5\) in the ConCov mobility model. These values give a reasonable balance between coverage and connectivity, and give both mobility models a similar coverage vs. time profile, allowing us to compare differences in their routing performances; see, for example, [7].  
_Coverage vs. Time_ in Figure 13 represents the total covered map area as a function of time, measured from the beginning of the evaluation period (1000 s). As expected, the higher density and speed of the nodes increase the area coverage rate. The Relay scheme has worse coverage than Pipe and ConCov-Pipe, since it reass  
Figure 14: Coverage Performance: \(C_{\flat}\) for three-target settings \(C_{4}\), \(C_{5}\) and \(C_{6}\)  
Figure 15: Coverage performance: Fairness for three-target settings \(C_{4}\), \(C_{5}\) and \(C_{6}\).  
operate as relay nodes in each route, reducing the number available to cover the map. This impact is more apparent at low density (30 UAVs). The longer the route lengths, the more UAVs are reassigned and the more coverage degrades; for example, in Figures 13 and 14, we see that Relay coverage is much worse at the target locations \(C_{4}\) than in \(C_{6}\). At low density (30 UAVs), TC-Pipe has slightly lower coverage performance because some UAVs are attracted to the pipe region. However, TC-Pipe allows the participating UAVs to move and cover the surrounding area, giving better coverage than Relay. For example, setting \(C_{4}\) at 30 UAVs and 20 m/s shows total coverage \(C_{v}\approx 90\%\) for Pipe and ConCov, reducing to 84% for TC-Pipe and to 73% for Relay (Figure 14). At a higher node density, TC-Pipe gives coverage comparable to Pipe and ConCov, while Relay stays worse, especially at longer route lengths (e.g., \(C_{4}\) and \(C_{5}\)). For example, for 50 UAVs at 20 m/s, TC-Pipe, Pipe, and ConCov achieve \(C_{v}\approx 98\%\), compared to Relay \(C_{v}\approx 91\%\).  
_Fairness (\(F\))_ performance is shown in Figure 15. Again, the higher density and speed usually increase \(F\), since all cells are visited more frequently. We find \(F\) is lower in TC-Pipe and Relay compared to Pipe and ConCov-Pipe for 30 UAVs at both speeds across all three location settings. In TC-Pipe, UAVs may be more attracted to regions along the flows due to pheromone masking, slightly decreasing coverage fairness. In Relay, UAVs are reassigned as relay nodes to form the routes, decreasing the number of UAVs performing coverage and, thus, coverage fairness. For example, with 30 UAVs in \(C_{4}\), Pipe and ConCov-Pipe give \(F\) values 0.74-0.67 (at 20 m/s) and 0.77-0.76 (at 40 m/s); these are slightly reduced in TC-Pipe to 0.66 and 0.74, and more significantly reduced in Relay to 0.58 and 0.63, respectively. With more UAVs, the fairness of TC-Pipe becomes comparable to Pipe and ConCov-Pipe, while Relay still shows lower fairness (Figure 15b). In single-target settings (not shown but available in [47]), the coverage and fairness performance of all schemes are comparable, since the effects of both topology control and relay nodes are smaller.