# Deep Validation of Spatial Temporal Features of Synthetic Mobility Models
## 2 Preliminaries
Correlated or group-based MMs [20]: In this class, the motion of nodes is correlated and related to the movement of other nodes. The whole network is split into many groups of nodes, where each group is governed by one leader. This may be a logical leader in the network or a pre-defined node, for example, the Reference Point Group Mobility (RPGM) model [21] or the Nomadic Community Mobility Model (NCMM) [22]. 3. Human or sociality-based MMs [23]: In this class of models, the nodes somehow follow human socializing behaviors, such as the Self-Similar Least Action Walk Mobility Model [24]. 4. Vehicular MMs [25]: In this class, the nodes imitate the movement of a real vehicle by taking into consideration stops at traffic signals, speed variation, and queuing along highways [26]. This category shows remarkable superior performance due to vehicular communications of intelligent transport systems which are applied in smart cities [27]. This family is not the focus of the validation performed in this paper. Regarding the first three classes, six different mobility models have been assessed and validated in order to inspect motion flaws in each category; that is, two entity MMs, two correlated MMs, and two human MMs have been considered. The most relevant properties of these patterns are as follows:  
* The first experimental pattern is that of the RWPMM, which belongs to the _random subclass of entity MMs_. It was first defined by [PERSON] and [PERSON] [28] and then became a benchmark for mobility modeling of mobile ad-hoc networks. Using this pattern, nodes choose a new speed that is uniformly distributed within an established range to move toward the next waypoint during a given period. The node stops at the destination for a specific pause time and then repeats the whole process until the experimental time has elapsed. This model takes into consideration neither previous mobility decisions nor the relation between time, speed, and distance [29]. This memoryless property leads to an unrealistic motion as a consequence of the abrupt stops and sharp turns [30]. * The second validated pattern is the Manhattan Grid Mobility Model (MGMM) [31]. This is an _entity synthetic mobility model with geographic restrictions_. It represents a variation of the pathway/city section mobility model [32]. In this case, mobile nodes move not randomly but according to existing roads and paths. The MGMM attempts to imitate the motion motif of mobile nodes on streets defined on maps. It uses a grid-road-based topology and a probabilistic method to generate node movements where a node can turn either left or right with the same probability of 0.25 or maintain a straight movement with a probability of 0.5 [33]. * The third validated mobility pattern is the RPGM model [21]. This model belongs to the class of _correlated or group-based mobility models_ where nodes behave in a co-operative manner and move in groups. Each group follows a reference point considered as the group leader, namely a node which may be a pre-defined leader or a center node. The motion of the entire group depends on the leader's movement, including its speed and direction. This model successfully mimics diverse mobility behaviors, such as battlefield movements and disaster relief. * The fourth verified mobility pattern is the Nomadic Community Mobility Model (NCMM) [22]. This model belongs to the class of _correlated or group-based mobility models_, which represent movement scenarios of a group where several nodes move together based on a single reference point. The whole group moves randomly from a given position to another one according to the mobility decisions of the leader. This mobility pattern is applied in mobile communications such as military applications, conferences, and museum visits by classes of students. * The fifth pattern is the Self-Similar Least Action Walk (SLAW) [34]. This model belongs to the _Human class_. It considers that people are usually attracted and confined to popular places that are often rare.