## 6 Conclusions  
In this paper, we have conducted a simulation-based study of the impact of mobility patterns on the performance of routing protocols in UAANET in the context of search missions. Similar studies in the  
Figure 17: PDR of flooding under various mobility models. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Protocol** & **RWP** & **EGM** & **ST** & **GM** & **RD** \\ \hline Flooding & 99.2 & 98.2 & 96.8 & 98.2 & 96 \\ RGR & 92 & 90 & 88 & 86 & 83 \\ AODV & 78 & 74 & 72 & 75 & 73 \\ OLSR & 76 & 69 & 65 & 68 & 61 \\ GRP & 54 & 48 & 45 & 45 & 40 \\ \hline \end{tabular}
\end{table} 
Table 3: Ranking of routing protocols’ PDR (%) over different mobility models. literature had a few limitations: lack of diversity in the considered routing protocols, lack of focus on searching missions-applicable mobility models or, most importantly, relatively low mobility that does not apply to UAANET. In an effort to eliminate the abovementioned limitations in our study, we have considered five random entity mobility models applicable to search missions, namely RWP, RD, ST, GM and EGM. Moreover, we considered four distinct routing protocols: AODV, a reactive protocol, OLSR, a proactive protocol, GRP, a geographic protocol, and RGR, a hybrid reactive-geographic protocol. In addition to those four protocols, we also considered simple packet flooding (_i.e._, no routing strategy) in order to single out the impact of certain mobility models' properties on the routing protocols and also to establish certain performance upper bounds. We have established that in UAANET, mobility models do affect the performance of routing protocols individually to almost the same extent as the ranking between the protocols' performance does not change when we go from one mobility model to the next. By means of protocol-independent metrics, we have shown that this impact of the mobility models can be explained and predicted. We can actually deduct a few rules with regard to this as follows: (i) a higher average number of neighbors coupled with a shorter average path length will lead to a shorter end-to-end delay; (ii) slow network topology fluctuations lead to lower routing overhead; (iii) a small network diameter coupled with a high clustering coefficient bring about a high PDR and shorter delay; (iv) the lower the average number of components, the higher the PDR. Finally, we have come up with a few conclusions/recommendations that were not drawn in the literature yet, to the best of our knowledge. These conclusions/recommendations provide a benchmark for future simulations of UAANET. Our main conclusions/recommendations are as follows. (i) We have enough reasons to strongly recommend not using the RWP mobility model for UAANET simulations: not only does it make unrealistic assumptions about the trajectory of a UAV, but also it results in poor coverage (the worst among all mobility models); therefore, it would not be suitable for the simulation of a thorough search mission of an entire region. We highly recommend using the EGM model instead, as it best mimics the movement pattern of UAVs. (ii) OLSR, a table-driven proactive routing protocol, and GRP, a position-based geographic routing protocol, are both three-times more sensitive than AODV, an on-demand reactive routing protocol, to the changes induced by the mobility models. (iii) GRP, a position-based geographic routing protocol, closely followed by OLSR, presents the worst routing performance over all of mobility models considered. This feeble performance points out the non-suitability of position-based geographic protocols and table-driven proactive protocols for UAANET. On the other hand, RGR, a reactive-geographic hybrid routing protocol, is best suited for UAANET. (iv) Based on the results of flooding, the upper bound for PDR for routing protocols to tend to is about 99.2%. Moreover, changing mobility models will lead to a PDR fluctuation of at least three percentage points. In a future work, we will investigate the effect of increased traffic on the routing performance in UAANET.