# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
## 2 Related Work  
The diverse range of FANET applications has prompted the need to investigate clustering and routing schemes under different mobility models [16], particularly collective motion [17] and random distributive motion [18]. The collective motion enables surveillance [19] in search and rescue missions [20], whereby a group of UAVs gather at a target location [21]. The random distributive motion models a multi-UAV swarm in an area for different purposes, such as collecting data from cellular users, transferring images and videos from a post-disaster area to BSs [22], and deploying an emergency network for recovering communication rapidly in a catastrophic area [23]. Meanwhile, the three-dimensional predictable distributive motion, which is investigated in this paper, is another mobility model in which UAVs move in randomly and uniformly distributed directions and velocities. Investigations have been made to investigate clustering and routing in FANETs. Clustering algorithms have been proposed to facilitate collaboration among UAVs and network stability, and an extensive survey of clustering, covering features, characteristics, competitive advantages, and limitations, can be found in [24]. Various routing algorithms have been proposed to increase network lifetime, and reduce energy consumption and the rate of link breakages. The routing algorithms can be classified into topology-based, position-based, hierarchical, deterministic, stochastic, and social network-based routing schemes [25]. Clustering and routing in FANETs must address the challenges of high mobility and limited residual energy while providing real-time communication between UAVs and ground control stations. Various tools have been applied to address the challenges of FANETs. In a network with high mobility, ensuring the link stability of a route helps to achieve network-wide stability, leading to improved network performances, such as a higher packet delivery ratio and a lower end-to-end delay. Game theory has been proposed for modeling and analyzing network problems [26]. Machine learning approaches have been proposed. In [27], dueling DQN is applied for managing the mobility of UAVs and planning flight path in a real-time manner while considering both delay and energy consumption requirements in dynamic Internet of things (IoT) sensor networks [28]. In [29], particle swarm optimization (PSO) is applied, and it is based on (a) the bounding box method to address the limited boundary of an area of investigation, and (b) the particle fitness function that takes account of inter-cluster distance, intra-cluster distance, residual energy, and geographic location when selecting CHs while achieving energy efficiency. In [30; 31; 32], swarm intelligence is used in clustering to achieve scalability. ```

**Algorithm 1**The DQN algorithm. In [30], the gray wolf optimization-based algorithm is applied to reduce localization errors in routing while achieving higher energy efficiency and localization accuracy, and minimizing flip ambiguity in the measurement errors of bounded distance. In [33], genetic algorithm with improved selection, crossover, and variation operators takes account of the bandwidth and stability of links and the residual energy of UAVs, leading to higher throughput and network stability, and a lower delay. In [34], fuzzy logic performs routing in two phases. First, in route discovery, the score of each UAV is calculated based on mobility, residual energy, and stability to (a) select routes with a higher fitness, and lower hops and delay, and (b) prevent the broadcast storm problem in which the flood of control messages to discover new routes is limited. Second, route maintenance. The second, minimizes route failure and reconstructs broken routes. Q-learning has been proposed to improve various aspects of routing, including selecting next-hop UAVs and routes, estimating link duration, and adjusting the Hello message interval and link holding time, contributing to improved efficiency and reliability in a highly dynamic FANETs [31].