# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 4 Evaluation
### Comparison of Experimental Results  
In this section, the simulation results of the MP-QGRD algorithm will be analyzed and compared with three routing protocols: QMR [35], QGeo [34], and GPSR [13]. QGeo combines Q-learning with geographic routing to evaluate the effectiveness of historical forwarding through Q-values, and prioritizes selecting neighboring nodes with high transmission success rates and stable paths. However, without considering node mobility and link interruption, and the Hello message being broadcasted at fixed intervals, it is difficult to adapt to rapid topology changes. QMR evaluates path quality (latency, link stability) based on Q-values and dynamically selects paths with higher Q-values to forward data. QMR takes into account energy consumption factors, but still uses a fixed cycle to send Hello messages, without considering link changes in high dynamic environments, resulting in delayed path selection. GPSR is a classic geographic location routing protocol that uses greedy and perimeter forwarding to prioritize the nearest neighbor node to the target, achieving fast path selection. However, neglecting link quality and stability can easily lead to the impact of link breakage, resulting in an increase in data loss rate. MP-QGRD combines motion prediction and Q-learning, using an extended Kalman filter (EKF) to predict the positions of adjacent nodes, evaluate link stability and duration, and adopt an adaptive Hello mechanism to detect the positions of neighboring nodes. At the same time, the algorithm introduces a Q-learning mechanism in routing decision-making, dynamically selecting the best performing node for data forwarding based on the link stability and transmission performance of adjacent nodes. We conducted multiple repeated experiments on four algorithms, comparing them in terms of packet delivery ratio, end-to-end delay, communication energy consumption, routing overhead. Among them, communication energy consumption refers to the average communication energy consumption of UAV nodes during the simulation process, measured in joules (J). The calculation method is to multiply the single communication signal transmission power by the single communication transmission duration and, finally, perform unit conversion. Figure 3 shows the evaluation metrics corresponding to a fixed number of 50 UAV nodes. Figure 3a illustrates how the packet delivery ratio (PDR) varies with node speed. As the node speed increases, the high speed of drones will lead to more unstable communi \begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Simulation area & \(1000\times 1000\times 1000\) m\({}^{3}\) \\ Number of nodes & \(20\)–\(100\) pieces \\ Node speed & \(10\)–\(100\) m/s \\ Physical layer & \(802.11\) p \\ Max TxPower (transmission power) & \(15\) dBm \\ MiniRssi (receiving sensitivity) & \(-80\) dBm \\ Signal propagation loss model & ITU-R 1411 log distance propagation \\ Packet size & \(64\)–\(1024\) Jytes \\ \(\tau\) & \(0.6\) \\ Communication radius & about \(250\) m \\ Mobile model & \(30\) Gaussian Markov Moving Model \\ the number of repetitions & \(20\)/ for each different algorithm \\ \(\gamma\) update interval & \(100\) ms \\ \(\lambda_{1},\lambda_{2},\lambda_{3}\) & \(0.4\) \(0.3\) \(0.3\) \\ Simulation time & \(300\) s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiment parameters. cation links, and the performance of all four routing protocols declines. Among them, the GPSR routing protocol exhibits the poorest routing performance. QMR, compared to QGeo, considers link stability, resulting in better packet delivery rates. Due to the faster speed and the worse link status, this protocol accurately predicts the location in the next moment using the extended Kalman filter model to deliver data to nodes with good link quality. Therefore, MP-QGRD demonstrates the best PDR performance. MP-QGRD reduces the average link interruption rate by 10% through mobile prediction and the adaptive Hello packet adjustment mechanism.