# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 5 Experiments and Analysis of Results
### Experimental Parameters and Results  
The experiment sets up 10 nodes that are respectively responsible for sending and receiving services, sending traffic at a constant rate from the beginning of the simulation. Comparison experiments are conducted for the same network scenario to test the performance of different routing protocols in network performance under different network size and mobile speed conditions, respectively. The network performance evaluation metrics are packet delivery rate (PDR), average end-to-end delay (average E2E delay), node average residual energy variance (\(Var_{E}\)), and percentage of node average residual energy (PRE). The parameters of the simulation experiment are shown in Table 4.  
Designing comparative experiments under two different dimensions of average node movement speed and the scale of network, three ad hoc traditional routes with better performance were selected: OLSR [15], AODV [18], and GPSR [21], with the proposed DDQN-MTGPSR algorithm in different scenarios for comparison testing. (1) Control variable: average speed of node movement. The effect of different movement speeds on the performance of the routing protocol is tested in NS-3. The initial average mobility speed is set to 15 m/s, incremented to 40 m/s in a gradient of 5, and the total number of nodes is fixed to 100 (moderate network size). As Figure 3 shows, as the speed of node movement increases, the overall packet delivery rates for several routing protocols show a decreasing trend, albeit to varying extents. The OLSR protocol, which is an active routing protocol that requires regular transmission of control information to maintain the routing table, experiences the most significant decline in packet delivery rates. This is due to the increased frequency of network topology changes with faster movement speeds, which makes OLSR unable to update routing information in time, leading to packet loss. In comparison, AODV, GPSR, and DDQN-MTGPSR show a slower decline in packet delivery rates as the node movement speed increases. Among these, GPSR experiences the lowest packet delivery rates at the same movement speed, while the differences in packet delivery rates between AODV and DDQN-MTGPSR are less pronounced, especially when speed increases. AODV is a demand-driven routing protocol that only establishes routing paths when data forwarding is required, thus ensuring more reliable forwarding paths and maintaining a relatively high level of packet delivery rates. DDQN-MTGPSR algorithm encourages the selection of higher-quality, less congested links, which have a lower likelihood of experiencing holes, \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Parameter Value** \\ \hline operating system & Ubuntu 20.04 \\ software version & NS-3.30.1 \\ transport layer protocol & UDP \\ comparative routing protocols & OLSR, AODV, GPSR, DDQN-MTGPSR \\ MAC/PHY layer protocol & IEEE 802.11b \\ radiant power & 20 dBm \\ transmission rate & 2 Mbps \\ packet transmission rate & 2.048 kb/s \\ packet length & 64 bytes \\ channel fading model & Friis propagation model \\ initial energy & 300 J \\ nodal distribution range & 2000 m \(\times\) 2000 m \\ node movement model & randomized waypoint model (RWP) \\ simulation time & 100 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Table of parameters related to simulation experiments. thereby reducing the risk of packet loss. Compared to the traditional GPSR, the average packet delivery rate of DDQN-MTGPSR is improved by 12.72%. As Figure 4 shows, when facing increased node mobility speeds, the Average E2E Delay of OLSR exhibits unstable oscillation behavior with the average node moving speed change. Its proactive management and updating of routing tables lead to frequent adjustments when nodes move at high speeds, necessitating the frequent updating of these tables, which in turn causes significant fluctuations in end-to-end delay. Among all, AODV experiences the highest end-to-end delay under the same conditions of node activity.