# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 3 System Design and Algorithm
### System Overview  
Figure 3 is an overall overview of the proposed system. Each UAV in the cluster extracts the geographic location from a sensor device such as GPS and converts the corresponding information into an IP address. Because UAVs can always determine their positions through positioning devices such as GPS, the algorithm proposed in this system can be used. Accordingly, the UAV can determine the IP address of another UAV in the cluster only by location information, without a routing table. After setting the IP address, the UAVs in the swarm form an ad hoc network. Through this process, the UAVs in the system are reliable and can communicate with each other much faster.  
The UAVs in the swarm that is set up deliver geographic location information to the UAV server, and the server creates a 3D simulation environment containing the received location information. Since this system does not use a routing table for data transmission, it does not select a path to be transmitted but broadcasts to all nodes within the network scope. Each node verifies that the received data are its own data.  
Since the broadcast method is used, data are transmitted to all neighboring nodes, and when each node broadcasts the transmitted data again, many unnecessary duplicate messages are transmitted. To solve this problem, our system sets the FR to determine whether the data are heading toward the destination through the previously created environment. In order to deliver data between UAVs in the cluster by the optimal route, the intermediate nodes check whether they are in the FR. The server calculates a forwarding angle that determines the FR through information on the start node, the arrival node, and the passing node. In addition, in order to further increase the probability of successful data transmission, the forwarding angle is learned in consideration of the characteristics of UAVs whose positions continuously change, so that there is no problem with data transmission even if the position deployed in the environment changes to a predictable  
Figure 2: Concept of GLAN.  
extent. By limiting the forwarding area using the forwarding angle, the flooding packet is reduced, the packet can be trusted, and the packet can be transmitted efficiently.  
The UAV server delivers the learned angle to all UAVs in the cluster, and each UAV sets routing rules by considering the location and angle of its own node according to the received angle. The established routing rules determine whether they are in the FR by considering their IP address, the IP address of the start node, and the IP address of the final destination node. If the node is in the FR, it continues to forward the data; otherwise, it discards the data. Through this process, the intermediate node can only serve as a router, reducing the time spent on data transmission and eventually reducing the network resources consumed. This system allows a drone in one location to exchange packets with a drone in another location without the need to run ad hoc routing. In addition, since broadcasting is used, data exchange is possible without a separate configuration or the exchange of information between UAVs, such as the link or node status, and the FR is optimized using RL to reduce unnecessary network resource consumption.