# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 2 Flying Ad Hoc Networks
### Mobility Models in FANETs  
The performance of routing protocols in FANETs is generally evaluated using testbed experiments or simulations in software environments. A testbed enables the performance of the designed routing protocol in real environments to be evaluated. However, owing to the high hardware cost, time requirements, and complexity of constructing large-scale networks with different topologies in testbed scenarios, software simulations are generally preferred for assessing performance. Software simulation-based evaluations of routing protocols in FANETs require a suitable realistic mobility model to define the optimal trajectories of each UAV in the swarm and to specify how their six DoFs (linear and angular velocity, position, attitude angles, acceleration, and direction) change with time in a 3D space. The mobility models in FANETs should be focused on the type of UAV swarm mission and be suitable for FANET applications such as surveillance, ground target searching and tracking, and search and rescue operations. The mobility model in FANETs should satisfy the following requirements: UAVs should autonomously adjust their flying directions and plan their displacements according to the mobility information of their neighboring UAVs and the location of the target at the ground terminal. Each UAV should maintain a particular safety distance to avoid inter-UAV collisions while simultaneously staying adequately close to ensure QoS in aerial connectivity. Each UAV should be able to join or leave the FANET arbitrarily. To preserve coordination and synchronization in movements, UAVs should continuously adjust their position and velocity according to the mean velocities of neighboring UAVs. The trade-off between maximizing sensing coverage and aerial connectivity [8] and self-healing to the failure of a neighboring UAV should be satisfied. The trajectory of each UAV in a swarm should be smooth, and the moving trajectory of each UAV should maintain fairness in terms of travel distance to maintain a balance in the energy consumption of UAVs within the swarm [44]. Additionally, when UAVs fly over the mission area, they should cover each zone evenly, and repetitive coverage should be avoided as much as possible to improve the search efficiency [44]. Finally, UAVs should be aware of external obstacles when adjusting their mobility. Owing to the above requirements, the mobility models in FANETs are unique and differ from the mobility models proposed for mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs). A realistic mobility model with a proper topology can jointly optimize the mission performance and communication performance. Several survey papers on FANET routing protocols have comprehensively reviewed mobility models in FANETs. In [30], the authors provided a novel taxonomy for mobility models in FANETs by classifying them into five different categories: random-based, time-based, path-based, group-based, and topology-based. In [31], the authors briefly discussed mobility models for FANETs, including random direction, random waypoint (RWP), reference point group mobility, Gauss-Markov, semi-random circular movement, Paparazzi, and smooth-turn mobility models. However, none of these mobility models adopt the collaborative and cooperative properties of swarm intelligence and they are mostly suggested for MANETs and VANETs [9]. Additionally, the aero dynamic constraints and UAV six DoFs are not taken into account by them [21]. FANETs differ from MANETs and VANETs in terms of their 3D mobility, node density, rate of topological alterations, and energy limitations. Both MANET and VANET nodes have two-dimensional mobility, and VANETs have higher mobility than MANETs. However, in VANETs, nodes are not energy-constrained, and their mobility is limited by roads [45]. Thus, the topology prediction in VANETs is much easier than that in FANETs.