# Flying Ad Hoc Networks: A New Domain for Network Communications
## 2 State of the Art
### Mobility Models  
A mobility model represents the movement of the nodes of a network, i.e., the changes in their position, speed, and acceleration over time in a delimited scenario. Mobility allows the nodes to adapt to the needs of each application, offering great dynamism and better performance depending on the selected mobility model. In addition, researchers can simulate FANETs with more realism using mobility models. Since nodes in FANETs are highly mobile, the choice of an appropriate mobility model for each simulation scenario is essential to evaluate the performance of the network before a real deployment to obtain results as real as possible [20]. We classify the existing mobility models into five different classes: pure randomized, time-dependent, path planned, group, and hybrid. * Pure Randomized Mobility Models: Random mobility models are the simplest and most common models to simulate the movement of nodes in an ad hoc network. Each node randomly selects its direction, speed, and time, independently of other nodes. In this group, the following mobility models stand out: Random Walk [21], Random Waypoint [17], Random Direction [22], and Manhattan Grid [23]. Figure 3 graphically depicts the characteristics of these well-known models. For instance, in the Random Waypoint mobility model (RWP), each node randomly chooses both its destination within the simulation area and the speed \(S\) between [_Smin_, _Smax_]. After this, the node starts moving at that constant speed towards the destination, and once the node reaches its destination, it remains static for a time _Tpause_, as shown in Figure 3b. Given that the RWP mobility model tends to group the nodes in the center of the simulation area, a node using the random direction (RD) mobility model searches for the edge of the area, remains static when arrives, and then selects a new direction angle (Figure 3c). * Time-Dependent Mobility Models: Movement is based on mathematical equations that depend on both the instant when the simulation is calculated and the state of each node to avoid sudden changes in speed and direction. The most prominent examples in this category are: Boundless Simulation Area [24], Gauss-Markov [25], Enhanced Gauss-Markov [26], 3-D-Gauss-Markov [27], and Smooth Turn [19]. The main disadvantage of the Boundless Simulation Area (BSA) mobility model (see Figure 5a) is that when a node disappears along an edge, it reappears on its opposite edge, which could be undesirable. On the other hand, the Gauss-Markov (GM) mobility model (see Figure 5b) is more realistic than the RW, RWP, and RD models, but it does not allow for modeling the behavior of FW-UAV, especially turns. The Enhanced Gauss-Markov mobility model (EGM) is a modification of the GM model specifically proposed for FANETs. The results obtained with this model are similar to the behavior that a FW-UAV would have while flying, since there are no sudden stops or sharp turns within the simulation. In addition, the EGM has a progressive boundary-avoidance mechanism to avoid edges, which allows soft curves at the boundaries. The three-dimensional Gauss-Markov mobility model (3D-GM) is another modification of the GM model designed for FANETs with the main characteristic of including mobility in the three dimensions, eliminating the bounce generated at the edges of the simulation area, as in the EGM model. Last, in the Smooth Turn (ST) mobility model (see Figure 5c), a node randomly selects a point along the line perpendicular to its heading direction and flies in circles around that point for a random time. * Path-Planned Mobility Models: These mobility models are characterized by nodes following a pre-calculated trajectory without taking any random direction. The nodes are forced to follow a series of movement patterns, only changing randomly between the different patterns that each model has. Within this category we have the Semi-Random Circular Movement [28] and the Paparazzi [18] models.