## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.1 Topology-Based  
Topology-based routing protocols do not require consideration of complex traffic types and load-balancing issues [26], and the algorithm is relatively simple. It relies only on the connectivity of the UAV nodes in the FANET (i.e., the topology) to plan routing paths, regardless of factors such as the type of network. Proactive, reactive, static, and hybrid topology-based routing protocols are categorized. The basic principles, advantages, and disadvantages of these protocols are shown in Figure 5.  
Figure 4: FANET routing protocols classification. Figure 3: The difference between traditional self-organizing networks and self-organizing networks in SAGIN. In proactive routing protocols, UAV nodes actively send route requests, thus finding reachable destination nodes and establishing routes. In other words, the routing table is updated as routes are changed and maintained at all communicating nodes. This type of routing is suitable for scenarios where the node density is low and the network topology is updated. Figure 5: Topology-based routing protocols. changes slowly [27; 28]. Similarly, static routing requires the routing table to be manually configured so that routing paths between nodes are specified, and the routing paths do not change as the network topology and node state change. This routing method is also only suitable for scenarios where the network topology is stable and the communication pattern between nodes is relatively fixed. In contrast to proactive routing and static routing, reactive routing means that when a node needs to send data to a destination node, it will only find a reachable destination node and establish a route by sending a route request [29]. For example, Dynamic Source Routing (DSR) [30] is a reactive routing protocol that uses a request-response mechanism to establish routing paths and carries routing information in the packet. When a packet reaches its destination, the destination sends a reply packet to the source node, which contains the routing path used to reach the destination. This type of routing is more suitable for scenarios like FANETs, where the node density is high and the network topology changes quickly. However, as reactive routing networks broadcast to find reachable nodes, they can put a strain on computing and storage resources. Hybrid routing uses both static and reactive or proactive routing to select the appropriate route for different UAV usage scenarios. The rapid movement of the UAV nodes in a FANET leads to frequent changes in the network topology, and the traditional routing algorithm takes into account a single factor in route selection, making it difficult to fully consider other performance parameters of the entire network. In addition, when the nodes undergo violent movements, traditional topology-based algorithms cannot respond to such changes promptly, which can easily lead to link disruptions. Therefore, traditional routing algorithms have been difficult to apply directly to highly dynamic FANET scenarios. However, many researchers have made targeted improvements to traditional routing protocols based on FANET characteristics. For example, the DSDV protocol needs to consider the effects of factors such as the limited energy of UAV nodes and link quality, and [PERSON] et al. [31] introduced the Bellman-Ford algorithm to improve it based on DSDV. [PERSON] et al. [32] proposed SAP-DSDV, which automatically adjusts the routing update period through the link stability between UAV nodes. [PERSON] et al. [33] implemented a random waypoint and Gaussian-Markov model integration to achieve improved QoS performance parameters for AODV, DSR, and DSDV protocols in FANETs. In general, operations such as calculating shortest paths and maintaining routing tables in topology-based routing protocols require significant computational and storage resources, which puts an overhead strain on resource-limited UAVs.