# Deep Validation of Spatial Temporal Features of Synthetic Mobility Models
## 2 Preliminaries  
Researchers are constantly eager to understand realistic mobility motions of mobile networks. Several traces are previously implemented to model real-life movements. In [14], the authors propose a new procedure aimed at collecting traces, while deployment phase failures are avoided, which improves the reliability of data. The introduced procedure makes possible the complete generation of traces with a minimum of damages without the need to recover mobile devices or losing them. Based on detecting and correcting all accidental issues in real-time, Morocco tracesoffer a set of relevant information about the vehicle status that was collected over seven months. The proposed procedure could be applied to generate vehicular traces. Likewise, it is suitable to record/generate human and animal traces. In [15], the authors studied data transfer opportunities between wireless devices carried by humans. They observed that the distribution of the intercontact time may be well approximated by a power law over the range [10 min; 1 day]. This observation is confirmed using eight distinct experimental data sets. In [16], pocket switched networks (PSNs) make use of both human mobility and local/global connectivity in order to transfer data between mobile users' devices. This falls under the delay tolerant networking (DTN) space, focusing on the use of opportunistic networking. One key problem in PSNs is in designing forwarding algorithms that cope with human mobility patterns. The authors present an experiment measuring the mobility of 41 individuals at the Infocom 2005 conference. The results exhibit a power-law distrbution for the time between node contacts. They then discuss the implications of these results on the design of forwarding algorithms for PSN. In [17], the authors define a methodology composed of three steps. First, the positions of 700 humans, spread over a metropolitan area, were anonymously traced during a special gathering event. Then, with a multi-disciplinary approach, they inferred the contact events from the these traces. Finally, they assessed the effectiveness of an ad hoc network established by the mobile phones to disseminate emergency information to the population in a timely fashion. The results reveal that human mobility can effectively enable emergency communications among a significant subset of mobile phones, although the connectivity of the network strictly depends on the number of cooperating devices and on the maximum allowed delay. In [18], the authors anonymously collected the positions of almost 2000 mobile phone users, spread over a metropolitan area greater than 200 km\({}^{2}\) for roughly one month. Then, with a multi-disciplinary approach, they estimated the mobility patterns from the collected data and, assuming Wi-Fi connectivity, inferred the contact events among the devices to evaluate the connectivity properties of a human-mobility-enabled wireless network. Until now, several synthetic mobility models have been suggested and are sometimes inspired from these mobility traces with the aim of investigating diverse mobility management in wireless sensor networks (WSNs). In this section, we will briefly introduce and discuss the role and main features of a synthetic mobility model before performing the validation of some models which belong to these classes. We then specify the main movement context of each pattern, which helps us to analyze the metrics' outcomes, which may display the mobility flaws of each model. Figure 1 shows a classification of mobility models that are grouped into four main classes. Figure 1: Synthetic mobility model classification. 1. Entity MMs [19]: In this class, each mobile node is autonomous, moving in the network independently of other nodes. This class may be further divided into four subclasses: random mobility models, models with temporal dependencies, models with spatial dependencies, and models with geographic restrictions. 2.