## 6 Conclusions  
UAV ad hoc networks offer a variety of benefits, including coverage expansion, agricultural monitoring, and search and rescue operations. Establishing a UAV ad hoc network requires determining the optimal path for packet transfer to reach its destination. Due to the frequent topology changes in typical UAV ad hoc networks, the selection of an efficient routing protocol is crucial for their successful deployment and management. This paper presents the various routing protocols proposed for UAV ad hoc networks. It evaluates and compares the performance of four routing protocols (OLSR, D-OLSR, ML-OLSR, and P-OLSR) in terms of packet delivery ratio (PDR), latency, energy consumption, and throughput. The analyzed scenario depicts a mission of search and rescue in which a rectangular search area has been set up and the task is completed more rapidly by using several UAVs. Furthermore, we take into account a randomized search strategy that is driven by a GM mobility model. However, the research does not take into account planned search schemes or circle search schemes. Three simulated scenarios were evaluated by altering the UAV's scalability, mobility, and allowable space. Based on the simulation results, ML-OLSR outperforms OLSR, D-OLSR, and P-OLSR in the considered measures. In addition, it was  
Figure 17: Throughput vs. allowed space. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Simulation Scenario**} & **PDR** & **Latency** & \multicolumn{2}{c}{**Energy Consumption**} & \multirow{2}{*}{**Throughput**} \\ \hline \multirow{3}{*}{**Best routing protocol**} & **Mobility** & ML-OLSR & ML-OLSR, P-OLSR & ML-OLSR & ML-OLSR \\  & **Scalability** & ML-OLSR & ML-OLSR & ML-OLSR, P-OLSR & ML-OLSR \\  & **Allowed space** & ML-OLSR, P-OLSR & ML-OLSR & ML-OLSR, P-OLSR & ML-OLSR \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the OLSR-based routing protocols based on the three simulation scenarios and performance metrics. demonstrated that OLSR performed significantly below D-OLSR and P-OLSR due to its high overhead and sluggish reaction to network topology changes. Future research directions will consider UAV routing protocols with machine learning and deep learning solutions to modify the MPR selection algorithm and select the optimal route between UAVs. Moreover, it is possible to improve the performance of routing protocols in UAV ad hoc networks using reinforcement learning (RL) and Q-Learning, which can be conducted first using the _ns-gym_ framework and then validated in real environments with real drones. An RL and Q-Learning system will offer real-time modification as it reacts to the environment in which the protocols are established and implemented. Meanwhile, _ns3-gym_ integrates both Open AI Gym and ns-3 to encourage the usage of RL in networking research [40]. Conceptualization, A.H.W. and R.N. ; methodology, A.H.W. and A.A.S. ; software, A.H.W. ; validation, R.N. and A.A.S. ; formal analysis, A.H.W., A.A.S., R.N. and D.K. ; investigation, A.H.W. and R.N. ; resources, A.A.S. and R.N. ; data curation, A.H.W., A.A.S. and R.N. ; writing--original draft preparation, A.H.W. ; writing--review and editing, A.H.W., A.A.S., R.N. and D.K. ; visualization, A.H.W. ; supervision, R.N. and A.A.S. ; project administration, R.N. and D.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.