## 4 Analysis and Perspectives
### Analysis of Other Issues  
FANET routing algorithms should not be limited to data transmission problems. Other issues, such as communication resource scheduling and collaborative scheduling, can also be modeled as routing problems. For example, for the resource scheduling problem, we need to specify the objectives and constraints of the scheduling problem first. Next, the tasks to be allocated and the types and amounts of resources available are identified. The nodes to be allocated resources are abstracted as communication nodes in the routing algorithm,  
Figure 8: Proportion of UAV routing algorithm research subjects and research data sources in the last 5 years. the minimum resource requirements are used as attributes, and the communication and collaboration relationships between resources are modeled as edges of the network. The routing algorithm is then used to select the path that is closest to the target value (or has the highest evaluation metric). Similarly, the dynamic deployment of service function chains based on the virtualization of network functions can be modeled as a routing problem. The two problems are situated in very similar contexts (e.g., the situation where a drone joins or leaves the network due to battery capacity or dynamic flight is similar to the situation where a node in the physical network fails and cannot continue to serve the virtual network function). We can try to abstract the physical network nodes as drone nodes and the communication links between the physical nodes as wireless communication links between the drones in the routing problem. For the deployment of service function chains, we can abstract them as path selection processes. In addition, the routing algorithm proposed for the FANET environment can be applied to satellite self-assembly networks or vehicle self-assembly networks, which also have highly dynamic characteristics. Specifically, the original routing algorithm for FANET can be replaced with constraints on flight speed, bandwidth limits, and so on, that are consistent with the characteristics of satellite and vehicular self-assembly networks. In general, the application of routing algorithms is very promising, and future researchers can try to model dynamic graph problems with a large number of constraints as FANET routing problems and use improved routing algorithms to solve them. In addition to the applications mentioned above, a large number of challenges remain with the FANET routing algorithm in the context of SAGIN. UAV collisions and signal loss are frequent problems in FANETs, so more complex network situations also need to be included in researchers' focus. The ability of routing algorithms to handle dynamic problems and uncertainty is also a critical aspect of measuring algorithm reliability and performance. Algorithms that can predict the behavior of drones should also be proposed for such problems. When algorithms have the ability to predict the actions and states of UAVs in advance, they can prepare UAVs to react accordingly in a targeted manner. SAGIN construction requires a large number of UAVs as relay nodes for air-based and ground-based networks, and an increasing number of applications rely on UAVs. However, a large number of studies have involved small-scale UAVs due to limitations in algorithms and experimental conditions; this does not match the trend of an increasing number of UAVs in future networks. Furthermore, because of its unique heterogeneity, self-organization, and time-variability, SAGIN has limited and unbalanced network resources in all three network segments compared to traditional terrestrial or satellite networks; in particular, FANET computational and storage resources have a large gap compared to terrestrial networks. This poses a challenge for protocol optimization, resource management, and allocation. Based on the previous analysis and discussion, it is easy to see that the development of machine learning offers new development opportunities for FANET algorithms.