# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 8 Simulation Results and Discussion
### Performance Evaluation
#### 8.3.1 Routing Performance
We can also see that the Pipe routing alone performs better than AODV at both node densities and speeds across all location sets because the pipe region facilitates proactive route switching instead of route failure and rediscovery when the route quality degrades. In addition, Pipe routing with the BS-CAP mobility model outperforms ConCov-Pipe (which performs Pipe routing but uses the ConCov mobility model [29]); BS-CAP mobility provides a better node degree and BS-connectivity than ConCov [7], making Pipe routing more effective. Figure 9: Routing Performance: PDR for single-target settings C\({}_{1}\), C\({}_{2}\), and C\({}_{3}\). Suffix “-20” (e.g., TC-Pipe-20, AODV-20) indicate UAVs at 20 m/s, while “-40” indicates UAVs at 40 m/s speeds. For a single target, the Relay scheme gives higher PDR than any other scheme, since it uses dedicated relay UAVs to establish and maintain the route. However, the trade-off is its reduced coverage performance. For three targets, particularly at higher densities and data rates, TC-Pipe may even provide better PDR than Relay. This happens because Relay finds a shortest path to the BS without considering congestion or interference, leading to poor PDR at higher data rates. More targets also mean more UAVs assigned as relays, which causes coverage performance to worsen (see Section 8.3.2). _Route Up (\(R_{u}\))_: A higher \(R_{u}\) indicates that the routes from the target UAVs to the BS exist for a longer time, which generally provides a higher PDR. Figure 11 shows the \(R_{u}\) values of each scheme for both node densities and speeds; we exclude Relay from the figure, since in general it achieves nearly 100% \(R_{u}\) by construction. We see that, out of the other schemes, TC-Pipe has the best \(R_{u}\) for both densities and speeds. For example, TC-Pipe achieves an \(R_{u}\) of 95% at 20 m/s and 92% at 40 m/s for location setting \(C_{4}\) and density 50. Figure 10: Routing Performance: PDR for three-target settings \(C_{4}\), \(C_{5}\), and \(C_{6}\). Using Pipe without TC drops these values to 86% and 81%, respectively, while AODV and ConCov-Pipe further reduce them. We can infer that all three aspects--topology control, pipe routing, and the mobility model--contribute to performance. A similar trend is seen at a node density of 30, although all schemes have reduced \(R_{u}\) values because there are fewer UAVs in the network. In single-target settings (\(C_{1}\ldots C_{3}\)), we see similar trends and values at a node density of 50, and similar trends with slightly higher values at node density of 30. **Route Breaks (\(R_{b}\)):** Figure 12 shows the number of route breaks incurred by each scheme at different densities and speeds. We can see that, across all conditions, the pipe routing schemes have far fewer breaks than AODV as proactive route switching within the pipe reduces interruptions and delays. Of these, TC-Pipe achieves the fewest route breaks across all settings, reducing new route discoveries and associated control overhead. Concretely, at locations C\({}_{4}\) for 50 UAVs at 20 m/s (Figure 12), TC-Pipe incurs \(R_{b}=24\), compared to 40 and 50 for Pipe and ConCov-Pipe, and 348 for AODV. Closer target locations tend to reduce route breaks, while higher speeds increase them, but otherwise, we find a similar performance trend across all settings.We observe similar trends and values in the  
Figure 11: Routing Performance: Route Up for three-target settings C\({}_{4}\), C\({}_{5}\), and C\({}_{6}\). single target settings, but omit them for space; more details can be found in [47]. Since the Relay scheme establishes stable routes by construction, it experiences zero route breaks.