## 4 Analysis and Perspectives
### Analysis of Experiments
In future research, reliable routing algorithms for large UAVs should continue to be designed for their large payloads, long endurance, and high-speed flight characteristics. Secondly, the algorithms should be designed in a way that multiple types and levels of UAVs are studied \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Network Scale (Sorties)** & **UAV Type** & **Method** & **Dataset Type** & **Simulation Platform** \\ \hline
[76] & 100 & Small drones & Fuzzy logic & Self-generated & NS-2 \\ \hline
[108] & N/A & Small drones & \
\begin{tabular}{c} Load-balancing \\ dynamic routing \\ algorithm \\ \end{tabular} & Self-generated &
\begin{tabular}{c} Windows+OPNET \\ +C++ \\ \end{tabular} \\ \hline
[105] & 2-35 &
\begin{tabular}{c} Medium to large \\ drones \\ \end{tabular} & GNN+RL & Self-generated & N/A \\ \hline
[132] & 10-200 &
\begin{tabular}{c} Medium to large \\ drones \\ \end{tabular} & DPSO & Self-generated & N/A \\ \hline
[133] & 50 & Small drones &
\begin{tabular}{c} Digital signature \\ algorithms \\ \end{tabular} & Self-generated & NS-2 \\ \hline
[134] & 60-100 & Small drones & RL & Self-generated & Python \\ \hline \hline \end{tabular}  
* N/A indicates that this item is not mentioned in the paper.  
\end{table} 
Table 1: _Cont._  
Figure 7: Trends in the publication of papers related to UAV routing algorithms from 2013 to 2022.  
simultaneously, as in [97; 119]. For example, faster UAVs should be deployed around nodes that are latency-sensitive, and UAVs with higher load capacity should be deployed around nodes with high storage resource requirements. Data source: Similarly, as shown in Figure 8, 79.2% of the studies used data generated by a simulation tool for their experiments, and only 20.8% used UAV data from a real environment for their experiments. The use of simulated data allows for easier control of the routing experimental environment and more flexibility in setting parameters and conditions while saving time and economic overhead; however, experimentation in real environments or with real datasets can reflect the adaptability of UAVs in complex, dynamic environments. We believe that future experiments investigating the design of routing algorithms for UAVs could include real datasets for validation or multiple sets of experiments using simulated and real datasets, respectively. Simulation platform: Many researchers used the more developed Network Simulator 2 (NS-2) and Network Simulator 3 (NS-3) as their simulation platforms of choice. Because NS-2 and NS-3 were partially developed in C++, it is challenging for researchers to implement protocols and debug the code. Additionally, they employ a time-driven simulation method, which is less effective for large-scale simulations. In the future, we ought to think about creating a simulation platform for SAGIN that can model networks at various scales or granularities. Routing methods: Most routing algorithms reflect an effort to optimize the employment of RL-related algorithms in recent years as RL research has gained in popularity. The application field of UAVs expands as the network size increases as businesses raise their investment in UAV research. Since it is nearly impossible to meet all constraints at once, future research should focus on the use of reinforcement learning algorithms in conjunction with the integration of other algorithms to create a routing algorithm that can be adaptive for the different QoS requirements of users. Additionally, UAVs might be utilized for tasks such as medical rescue and event security, which would include a lot of sensitive data and put significant pressure on the drone routing algorithms' security. Therefore, exploration of a more secure routing algorithm will also become a hot topic. Researchers can use federated learning techniques to encrypt and train data inside the drone nodes to protect private data, and finally submit the trained sub-models to a central server for aggregation and federated learning.