## 3 Classifications of Routing Protocols in UAV Networks
### Location-Based Routing Protocol  
In UAV networks, due to the high mobility of nodes in the network, it is difficult to maintain routing tables. At the same time, traditional routing protocols incur significant costs by repeatedly finding routes before sending packets. Therefore, routing protocols based on geographic location information have been studied to address these challenges. The advantages of geolocation-based routing protocols are more suitable for highly dynamic networks, including UAV communication networks. Some prominent studies for location-based routing solutions are described in detail below. The Greedy Perimeter Stateless Routing (GPSR) protocol is a routing protocol that relies on the location [49] of routers and the destination of packets to make packet forwarding decisions. By keeping only the local topological state, GPSR scales better in a per-router state than the shortest path routing and arbitrary routing protocols as the number of network destinations increases. Under the condition of frequent network topological changes of mobility, GPSR can use local topological information to find new accurate routes quickly. Based on the well-known GPSR protocol, the authors in [50] developed a GPSR with Position Prediction and Uncertainty (GPSR-PPU) algorithm, which works by adding location prediction and uncertainty to the well-known GPSR algorithm. GPSR-PPU improves the selection of the next-hop node in highly mobile and noisy UAV environments. The simulation results show a 33% improvement in packet loss, a 12% reduction in overhead, and a 42% reduction in delay. Geographic Load Sharing Routing (GLSR), proposed by the authors in [51], is an extension of GPSR. In the GLSR algorithm, when forwarding packets to a given destination, a node considers not just one but a set of next candidates, and allocates traffic among them based on queue dynamics, thus making the path more reliable. In addition, load balancing is performed using a congestion-aware handover strategy. In [52], the authors developed another geolocation-based routing protocol, RGR (Reactive-Greedy-Reactive), which takes advantage of the readily available location information of UAVs. The RGR algorithm is a combination of the mechanisms of Greedy Geographic Forwarding (GGF) and reactive routing. The proposed RGR uses the location information of UAVs, as well as reactive end-to-end paths, in the routing process. The simulation results show that RGR outperforms AODV protocols in UAV tasks in terms of criteria such as latency and packet delivery ratio. During the operation of RGR, GCF is triggered when the established route to the destination is suddenly interrupted. Since the UAV devices in the network move dynamically, they lead to rapid topological changes. Existing geolocation routing protocols need to send beacon packets (or hello packets) frequently to maintain the accuracy of routing selection. However, high beacon frequency means high overhead, leading to collisions with data packets and transmission delays. To overcome this problem, the authors in [53] proposed an Adaptive Beacon Prediction Protocol, called ABPP, that allows flexible adjustment of the beacon frequency and predicts the future location of the UAV. Figure 12 illustrates the situation in which the