# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 1 Introduction  
In recent years, unmanned aerial vehicles (UAVs) have been widely used in military and civilian fields, ushering in a new development stage [1]. Flying ad hoc networks (FANET) [2] have emerged as a unique wireless communication network construction method, demonstrating enormous potential. FANETs can achieve self-organizing network construction of flight nodes without pre-establishing a static network, with flexible node deployment, rapid networking capabilities, strong adaptability, and easy implementation characteristics [3]. However, the high-speed mobility and high energy consumption of flying nodes also bring about a series of severe challenges [4]. The high-speed movement of UAVs' nodes leads to continuous variations in network topology and frequent link interruptions, further resulting in increased uncertainty and latency in data transmission [5]. Existing works generally have the problem of high-frequency routing detection consumption [6], especially the frequent sending of Hello packets in fixed time slots [7, 8], which leads to large communication energy consumption and wireless link congestion [9]. At the sametime, the fixed exploration mode lags behind the position perception of neighbor nodes, which affects the accuracy of routing. Some studies [10, 11] indicate that, in FANET, frequent long-distance communication will significantly affect the overall energy consumption of UAVs, and unbalanced energy consumption may lead to the failure of routing paths, thus shortening network stability. In order to solve the problem of energy limitation, this article integrates the motion information and remaining energy level of UAVs into the routing process to balance communication and flight requirements [12], thereby avoiding excessive energy consumption and network instability. Considering the relationship between energy consumption and flight stability, the routing decision maximizes network stability while ensuring communication quality by reasonably predicting the motion trajectory and energy state of nodes [13]. To address the issues of frequent link interruptions and high energy consumption caused by the frequent changes in node position in FANETs, this paper proposes a carefully designed routing protocol, divided into three key components: mobile prediction, neighbor discovery, and routing decision. Neighbor mobile prediction can accurately perceive the flight status of neighboring nodes and dynamically adjust the Hello packet sending gap. Adopting reinforcement learning methods [14], routing decisions are made by comprehensively considering link stability, communication energy consumption, and distance factors [15]. This innovative design enables better adaptation to the environment of frequent node position changes and makes more accurate and intelligent decisions. The contributions of this paper are as follows:  
1. By introducing the extended Kalman filter (EKF) prediction model [16], the prediction of node positions in flying ad hoc networks has been achieved, reducing the problem of position failure caused by the high dynamics of nodes in the time slot interval of position detection. 2. Designing a mechanism for dynamically adjusting the Hello packet sending gap, optimizing the neighbor discovery process, reducing the neighbor discovery requirements of high-speed mobile nodes, and significantly reducing routing detection overhead. 3. Introducing a Q-learning algorithm with adaptive adjustment of the learning rate and discount factor for intelligent routing decision-making. Taking into account link stability, energy, and distance metrics, a reward function is designed to maximize the reward for routing decisions, thereby improving network performance and stability. The remaining structure of this paper is as follows: In Section 2, related work on routing protocols for current FANETs is summarized. Section 3 introduces the network model, and provides the design and performance analysis of the MP-QGRD algorithm. In Section 4, the experimental results are presented and analyzed.