# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 2 Review of Related Works  
The ad hoc network mobility models serve as the cornerstones of network design, performance evaluation and routing protocols design, etc. For example, [PERSON] et al. in [5] proposed a smooth turn mobility model, which can capture the acceleration correlation for UAVs over different time and positions while failing to consider the randomness. Currently, extensive studies have been conducted and some random mobility models have been proposed, such as the widely used random walk (RW), random direction (RD), random waypoint (RWP) models [6; 7]. Specifically, the RWP model assumes that all nodes are independent and nodes are assigned with random destinations and velocities. Once arriving, they pause for a random break before proceeding to the next random destination. These works take the stochastic process into account to emulate the random deviation or jitter due to external interference. Nevertheless, they fail to depict the correlation between two adjacent times. In other words, the speed is influenced by the previous state. In [8], [PERSON] et al. summarized the mobility models that are widely used in the simulations of ad hoc networks, including the Gaussian-Markov model, which model current speeds and movement directions based on the corresponding former speeds and directions. The network topology architecture is a foundation for further FANET management, and unreasonable networking may increase the communication overheads, and even jeopardize or paralyze the entire network. Considering the existence of multiple mobile nodes, considerable researchers have focused on it and proposed some classical models. In [1], [PERSON] et al. summarized the three typical network topologies of FANET, i.e., the star topology, mesh topology, and the cluster-based hierarchical topology. In contrast to the star topology structure, the mesh topology possesses merits of high flexibility and autonomy and it can overcome the high link path loss or interruptions in some harsh scenarios via multiple hops. However, it requires quantities of overheads to realize data packet transmission via multiple hops. Regarding the cluster-based networking methods, this hybrid hierarchical topology can evidently reduce the management complexity and communication overheads. Furthermore, this scheme can group nodes into different clusters based on different application scenarios and arrangement strategies. Nonetheless, it encounters some new challenges, such as the determination of number of cluster and the election of CH. In [9], [PERSON] et al. proposed a border patrol clustering algorithm (BPCA) via a cooperative architecture, which optimizes the CH selection by taking the effects of relative speed, separation distance, and nodes' movement model into account. In [10], [PERSON] et al. introduced a FANET simulator, which considers human mobility behaviors, UAV/drone behavior models, and energetic issues. In highly mobile scenarios, the network needs frequent updates to maintain a stable structure, which leads to an increase in control overhead and transmission latency. To tackle the mobile scenario, the authors in [11] proposed a FANET clustering algorithm based on the \(k\)-means model. It considers both the mobility and relative positions to boost the network reliability. Furthermore, the authors derived the optimal cluster sized based on the maximum coverage probability of CH. In [12], [PERSON] et al. determined the cluster number based on bandwidth balance and used the \(k\)-means algorithm to group all nodes. Then,they proposed a deep Q-learning (DQN)-based cluster head selection algorithm. Another promising method is to predict the node information to assist further clustering. For example, [PERSON] et al. in [13] proposed a mobility-prediction-based clustering algorithm by using the attributes of moving UAVs. Specifically, they presented a dictionary trie structure prediction algorithm and link expiration time mobility model. In [14], [PERSON] et al.