# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 2 Flying Ad Hoc Networks
### Mobility Models in FANETs
In FANETs, UAVs have mobility with six DoFs, the presence of external obstacles, limited energy, system stability to ensure stable links, and restricted trajectories owing to collaborative motion planning. Thus, the mobility models proposed for MANETs or VANETs are not suitable for application to FANETs. Motivated by these limitations of previous studies, we provide a brief review of realistic FANET mobility models that mimic the characteristics of a swarm according to recently published research articles. In [9], the authors proposed a novel mobility model for FANETs inspired by bold flocking. They defined the neighbors of UAVs into three categories by dividing the transmission range of UAVs into three zones: repulsive, stable, and attractive. They updated the accelerations, velocities, and positions of UAVs by applying seven different rules: separation, cohesion, alignment, centripetalism, consistency, and synchronization. Each of these rules defines one motion component, and the summation of these rules defines the optimal mobility for each UAV in the swarm. This behavior-based mobility model, inspired by bird flocks, supports both the coverage and connectivity requirements of FANETs with inter-UAV collision avoidance. Thus, it is suitable for performing surveillance, target searching, and rescue operations in emergency scenarios. Similarly, in [4; 10], the authors proposed a virtual force-based mobility model for FANETs by applying four different virtual forces to optimally deploy UAVs as an ABS. The virtual forces they considered to obtain the position and velocity for each UAV in the swarm were attractive forces toward the hot spot areas of GUs, attractive forces toward the isolated GUs, repulsive forces to neighboring UAVs to avoid inter-UAV collisions, and repulsive forces to external obstacles to avoid collision with external obstacles. The sum of these virtual forces defines the acceleration, velocity, flying direction, and position of the UAVs. This virtual force-based mobility model supports optimal coverage of the GUs and simultaneously maintains stable bi-connectivity in the aerial network. Additionally, it can generate a smooth trajectory for UAVs while maintaining fairness in the travel distance between UAVs in the swarm. The adaptive adjustment of the attractive and repulsive force weights can effectively manage swarm stability, connectivity, and obstacle avoidance [11; 17]. In [18; 19], the authors proposed a mobility model for FANETs inspired by [PERSON]'s law of springs, which is known as the virtual spring-based mobility model. According to [PERSON]'s law, they assumed that the force is proportional to spring deformation. In [18], they proposed a UAV positioning method in a hexagonal pattern to maximize the coverage under QoS in connectivity by defining the attractive and repulsive virtual spring force laws. If the inter-UAV distance crosses the minimum threshold, the spring force is repulsive. In contrast, if the inter-UAV distance crosses the maximum threshold distance, the spring force is attractive. By computing the summation of all virtual spring forces with all neighboring UAVs, each UAV determines the optimal mobility. This type of mobility model is suitable for surveillance missions. Similarly, in [19], the authors utilized a virtual spring force mobility model to maintain a strong aerial mesh network to provide communication in a disaster scenario. Although this type of mobility model can support coverage and QoS in aerial connectivity, it is not well-suited for external obstacle avoidance. In [12; 16; 46], the authors proposed a mobility model using an APF. APFs have attractive fields for neighboring UAVs, repulsive fields to maintain the minimum separation distance with neighboring UAVs, and repulsive fields to avoid external obstacles. The fields are computed according to the inter-UAV distance and the distance between the UAV and obstacles. The optimal mobility of UAVs can be obtained by obtaining the negative gradient of the net artificial potential field.