## 1 Introduction  
The movement pattern of unmanned aerial vehicles (UAVs) [1] in UAANET depends on the type of application for which they are being used. For example, a mobility model that allows the nodes to move independently from one another is more suitable in searching missions where a large area has to be scoured. Mobility models can be classified into two main groups: path-defined mobility models and random mobility models. In path-defined mobility models, a path is predefined for the node to follow. The positions that the nodes are going to go through are set in advance. In random mobility models, the positions are computed \"as we go\". Such models are more suitable for searching missions where the information available about the searched area is minimal. Random mobility models can be further classified into two main groups: group mobility models and entity mobility models. In group mobility models, the nodes move as a group, following a group leader. Conversely, in entity mobility models, the nodes move independently from one another. Since our main interest is in searching missions in UAANET, this paper focuses on random entity mobility models. The deployment of UAANET is still not widespread nowadays. Consequently, real-life measurements for pure research purposes are mostly non-existent and, where available, can be very expensive. Therefore, the research community has to rely on simulations as an alternative in order to develop and evaluate routing protocols. The mobility models used in simulations play a central role in such a context. It actually would be nice to design routing protocols that are insensitive to mobility patterns and that would perform well regardless of the mobility pattern used in the simulation. Since this is mostly not the case, this paper conducts an in-depth experimental study to understand how and why mobility models affect the performance of routing protocols in UAANET in the context of search missions. An accurate evaluation of the impact of the mobility models on the performance of routing protocols requires testing multiple mobility patterns and different routing protocols. Otherwise, the observations made and the conclusions drawn from the simulation studies may be misleading. For that reason, we are considering five distinct random entity mobility models in this paper: the random waypoint (RWP) [2], the random direction (RD) [2], the smooth-turn (ST) [3,4], the Gauss-Markov (GM) [5] and the enhanced Gauss-Markov (EGM) [6] models. RWP is widely used for simulations of MANET due to its simplicity and its availability in almost all simulators. However, this model is very unrealistic, since it features sudden stops and sharp turns. Due to mechanical and dynamics constraints, UAVs cannot turn abruptly nor stop/accelerate suddenly. RD, which is fairly similar to RWP, is just as unrealistic as RWP for the same reasons. ST and GM were proposed in order to overcome these sharp turns and sudden stops problems. However, they still feature sharp turns at the boundaries. To solve this problem, we proposed EGM as part of our previous work [6]. For the routing protocols, we are considering four very distinct protocols: _ad hoc_ on-demand distance vector (AODV) [7, 8], optimized link state routing (OLSR) [7, 9], reactive-geographic hybrid routing (RGR) [10, 11, 12, 13] and geographic routing protocol (GRP) [14]. AODV is a reactive on-demand routing protocol, whereas OLSR is a table-driven proactive routing protocol. GRP is a geographic routing protocol, and RGR is a hybrid reactive and geographic routing protocol that we developed in a previous work [10, 11, 12, 13]. We also consider simple packet flooding for further understanding. For our analysis, we first define a set of protocol-independent metrics to characterize and compare the different mobility models. These metrics will also help to explain the different performance shown by the routing protocols under various mobility models.