# Flying Ad Hoc Networks: A New Domain for Network Communications
## 2 State of the Art
### Mobility Models
The former (SRCM) is a suitable model to simulate the behavior of UAVs that are flying over a point in circles with different radius collecting information, as shown in Figure 2a. The latter (PPRZM) is a stochastic mobility model based on the Paparazzi autopilot software for UAV [29]. This Paparazzi autopilot presents five possible movements: _Stay-At_, _Eight_, _Way-Point_, _Scan_, and _Oval_ (represented in Figure 2b). Thus, the PPRZM mobility model is based on a state machine where each state is one of the possible movements that a UAV can do. The state machine is showed in Figure 2c. * Group Mobility Models: They are based on the fact that the different nodes of the network move together around a common point. This behavior has great advantages, especially in the field of FANETs, where this approach can be used in many applications. Three representative examples can be found in this category, namely, Exponential Correlated Random [30], Particle Swarm [31], and Reference Point Group Mobility [32]. The Exponential Correlated Random (ECR) mobility model is similar to the time-dependent mobility models. To control the groups of nodes a set of variables is added, whose configuration is not straightforward. The Particle Swarm Mobility Model (PSMM) is based on the iterative optimization method Particle Swarm Optimization (PSO) [33]. This model calculates the speed and direction of each node according to the previous speed/direction and the position in which nodes are located with respect to the reference point. The operating principle of PSMM is illustrated in Figure 4a. In the Reference Point Group Mobility (RPGM) model, nodes are grouped into clusters. Each cluster has a center, which can be a logical center or a leading node. The center follows an RWP mobility model and the nodes will be moving around the center (see Figure 4b). This mobility model has numerous variants, the most important ones are Column (CLMN) [21] (see Figure 6), Nomadic Community (NC) [21] (see Figure 7), and Purse [21]. For instance, nodes in the Purse (PRS) mobility model try to catch a target, which may be another node or an imaginary point as represented in Figure 8. As stated by its authors, [21] \"you have an apple-pie in your hand and some flies are trying to land on it. You move the pie to avoid them to reach it... Flies are the nodes.\"
* Hybrid Mobility Models: This set of hybrid models is usually based on a combination of previous models. As an example, the Hybrid Markov Mobility Model with Pheromones (H3 MP) [34] is based on the Markov and the Distributed Pheromone Repel [35] models joining the advantages of both. Whereas Markov chains improve the overall behavior of the UAV, the pheromone approach allows a more precise control of mobility in a local and dynamic way by sharing knowledge among UAVs. Figure 3: Pure randomized mobility trajectories: (**a**) Random Walk mobility model where each node selects a random direction [0, 2\(\pi\)) and speed \(S\) [_Smith_, _Smax_] that is maintained for a predefined time interval \(t\) or a constant distance \(d\) (500 \(\times\) 500 m, \(t\) = 5 s); (**b**) Random Waypoint (_Tpause_ = 3 s); (**c**) Random Direction, as in RWP but the node searches the edge point of the simulation area; and (**d**) Manhattan Grid with example of an urban scenario with 16 horizontal and 16 vertical streets. Figure 2: Path-Planned mobility trajectories: (**a**) Semi-Random circular mobility model; (**b**) Paparazzi autopilot UAV Movements (_Stay-At_, _Eight_, _Way-Point_, _Scan_, and _Oval_); and (**c**) PPRZM state machine. Figure 4: Column mobility models: (**a**) Example of a single step in PSMM; (**b**) Trajectory simulation using RPGM. Figure 5: Time-Dependent mobility trajectories: (**a**) Boundless Simulation Area, (**b**) Gauss-Markov, and (**c**) Smooth Turn.