# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 5 Experiments and Analysis of Results
### Experimental Parameters and Results
GPSR and its improved version, DDQN-MTGPSR, exhibit decreasing latencies, mainly due to the on-demand nature of AODV's routing establishment, which can cause additional delays when the network topology changes rapidly. Traditional GPSR makes routing decisions based on the next hop's location without going through a full routing discovery process, leading to generally lower delay compared to AODV. The DDQN-MTGPSR algorithm balances multiple routing performance indicators, showcasing a superior end-to-end delay compared to GPSR and AODV, especially under certain node mobility speeds, approaching the OLSR performance. In comparison to traditional GPSR, the end-to-end delay of DDQN-MTGPSR is, on average, reduced by 20.05%. Figure 4: Variation of average E2E delay with average speed. Figure 3: Variation of PDR with average speed. As Figure 5 shows, the increase in \(Var_{E}\), due to node movement speed, when using OLSR with DDQN-MTGPSR, is not notably significant. OLSR boasts an active routing and multi-point forwarding mechanism, which distributes network control messages and energy consumption evenly across nodes. Even as the speed of nodes increases, the energy consumption per node stays relatively consistent, rendering the variance in energy consumption insensitive to velocity changes. DDQN-MTGPSR incorporates the remaining energy level of neighboring nodes, prioritizing the selection of nodes with higher remaining energy for forwarding, thus helping to balance energy consumption among nodes. Despite DDQN-MTGPSR's introduction of link quality and routing hole, which enhances the connectivity, it still operates based on local topologies, leading to slightly higher variance compared to OLSR. Comparatively, AODV and GPSR display consistently higher variances. AODV is an on-demand routing protocol, and GPSR relies on local information of one-hop neighbor locations, neither of which can meticulously obtain the entire network topology or achieve an even distribution of energy usage. This can lead to some nodes shouldering more of the routing discovery and data forwarding tasks, contributing to an uneven distribution of energy consumption. However, GPSR exhibits lower variance than AODV thanks to its location-aware routing strategy, which more efficiently adapts to topological changes when dealing with rapidly moving nodes. It quickly directs attention to the next hop closer to the destination, avoiding the additional energy expenditure that arises from searching for new routes, a characteristic that AODV frequently engages in during these scenarios. The average residual energy variance of nodes for DDQN-MTGPSR is reduced by 50.15% on average as compared to GPSR. As Figure 6 shows, when the speed is low because GPSR does not need to maintain a complex routing table, it maintains a higher PRE. In a highly dynamic network, AODV requires frequent route discovery, and OLSR needs to continuously update routing information so the remaining energy is lower than that of GPSR. Under low-speed conditions, the topology changes are not too frequent. Although DDQN-GPSR introduces various environmental states to improve network performance, at the same time, its broadcast packets have been changed, and the fields are longer, so the overhead is slightly higher than that of GPSR. However, at high speeds, GPSR incurs increased overhead due to frequent switching to the recovery mode, while DDQN-GPSR has more selectable neighbors and higher routing efficiency, thereby reducing the overall overhead compared to GPSR. Compared to GPSR, the PRE of DDQN-MTGPSR has increased by an average of 0.47%. Figure 5: Variation of \(Var_{E}\) with the average speed. In summary, compared with GPSR, the DDQN-GPSR routing protocol comprehensively improves various indicators. At the same time, the experimental results show that OLSR and AODV are not suitable for extremely high-speed scenarios, and the packet delivery rate of the former and the delay of the latter are prone to negative performance due to the change in moving speed.