###### Abstract  
Unmanned aerial vehicles (UAVs) are important equipment for efficiently executing search and rescue missions in disaster or air-crash scenarios. Each node can communicate with the others by a routing protocol in UAV ad hoc networks (UANETs). However, UAV routing protocols are faced with the challenges of high mobility and limited node energy, which hugely lead to unstable link and sparse network topology due to premature node death. Eventually, this severely affects network performance. In order to solve these problems, we proposed the deep-reinforcement-learning-based geographical routing protocol of considering link stability and energy prediction (DSEGR) for UANETs. First of all, we came up with the link stability evaluation indicator and utilized the autoregressive integrated moving average (ARIMA) model to predict the residual energy of neighbor nodes. Then, the packet forward process was modeled as a Markov Decision Process, and according to a deep double Q network with prioritized experience replay to learn the routing-decision process. Meanwhile, a reward function was designed to obtain a better convergence rate, and the analytic hierarchy process (AHP) was used to analyze the weights of the considered factors in the reward function. Finally, to verify the effectiveness of DSEGR, we conducted simulation experiments to analyze network performance. The simulation results demonstrate that our proposed routing protocol remarkably outperforms others in packet delivery ratio and has a faster convergence rate. 1  
DQN with Prioritized Experience Replay-Based Optimized Geographical Routing Protocol of Considering Link Stability and Energy Prediction for UANET  
mentioned above in UANETs, we must design an adaptive and intelligent routing protocol to select the next hop for better network performance in UANETs. The primary purpose of routing is that a source node sends packets to a destination node by forwarding through some intermediate nodes. There are three categories concerning routing protocols: traditional routing protocols, heuristic routing protocols and reinforcement-learning (RL)-based routing protocols. Traditional routing protocols mainly include four classes: (1) Proactive protocols, such as optimized link state protocol (OLSR) [8], which is a table-driven method for forwarding packets in a timely manner based on global network topology. However, it needs to frequently update routing tables, resulting in excessive overhead. (2) Reactive routing protocols, like ad hoc on-demand distance vector (AODV) [9], which can reduce overhead, but it takes a long time to re-establish a new route when the network topology changes or route failure occurs. (3) Hybrid protocols, the like zone routing protocol (ZRP) [10]. This combines the advantages of proactive and reactive routing. However, it is challenging to maintain the gathering of information concerning high-dynamic nodes and link behavior. (4) Geographic location protocols, for instance, GPSR [11], which utilize the location information for greedy forwarding of packets and perimeter forwarding when faced with a void area. Although GPSR is superior to non-position-based routing protocols, perimeter forwarding causes large delays which may severely impact network performance. In summary, due to the unique features of UANETs, it is tough to adapt these protocols to highly dynamic UANETs. Many studies have proposed modifications of the traditional routing protocols to adapt to UANETs [12; 13]. Although traditional routing protocols are simple and easy to implement, their major drawbacks are a lack of intelligence and autonomy in terms of adapting to high-speed mobility, complex environments, and varied flight tasks in UANETs. Some researchers have been inspired by laws of nature or experience with specific problems, proposing heuristic routing protocols. In [14; 15], a genetic algorithm-based routing protocol to improve the efficiency of MANET was proposed. Ant colony optimization has been used for routing protocol [16; 17].