## 3 Classifications of Routing Protocols in UAV Networks
### Swarm-Based Routing Protocol
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Ref.** & **Algorithm** & **Main Contribution** & **Advantage** & **Limit** \\ \hline Location prediction & [50] & GPSR-PPU & \begin{tabular}{c} Application in UAV \\ networks to \\ support firefighting \\ \end{tabular} & \begin{tabular}{c} Improved next-hop node \\ selection in highly mobile \\ and noisy \\ FANET environments \\ \end{tabular} & Depends on \\ location accuracy. \\ \hline Geographic load sharing & [51] & GLSR & \begin{tabular}{c} Solving the bottleneck \\ problem at Internet \\ gateways in \\ airline networks \\ \end{tabular} & \begin{tabular}{c} Exploit aircraft position \\ information (based on GPS) \\ together with buffer size \\ information to exploit the \\ full A2G capabilities \\ available and optimize \\ throughput in the network \\ \end{tabular} & \begin{tabular}{c} Performance depends \\ heavily on node density \\ and node distribution; in \\ environments with many \\ distributed nodes, GPSR \\ may not be effective \\ \end{tabular} \\ \hline Geolocation combined with & [52] & RGR & \begin{tabular}{c} Application in Unmanned \\ Aerial Ad-hoc Networks \\ (UAANET) \\ \end{tabular} & \begin{tabular}{c} The proposed RGR uses the \\ UAV location information \\ as well as the reactive \\ and-to-end paths during \\ routing to minimize the \\ delay in the network \\ \hline Based on prediction & [53] & ABPP & \begin{tabular}{c} Application in FANET \\ networks with fast and \\ flexible moving nodes \\ \end{tabular} & \begin{tabular}{c} ABPP, can flexibly adjust \\ the beacon frequency and \\ predict the future position \\ of UAV and the accuracy of \\ routing choice. \\ \end{tabular} & \begin{tabular}{c} Performance depends on \\ the accuracy of \\ the prediction; a trade-off \\ between beacon frequency \\ and cost is required \\ \end{tabular} \\ \hline Based on 3D location & [54] & OLSR & \begin{tabular}{c} Application in UAV \\ networks with fast and \\ flexible moving nodes \\ \end{tabular} & \
\begin{tabular}{c} Optimize energy efficiency \\ by considering the \\ remaining energy and node \\ level of the UAV and \\ frequent structural changes \\ (based on the 3D position \\ information of the UAV) \\ \end{tabular} &
\begin{tabular}{c} High computational cost; \\ requires accurate and \\ continuously updated 3D \\ location information \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prominent location-based routing algorithms. The results demonstrate that the method consistently achieves shorter, safer, and more energy-efficient flight paths in the same number of iterations. A bee swarm routing algorithm applied to UAV networks (BeeAdhoc) was also proposed by the authors in [57]. The bee population can be divided into two factions, including observer bees and forager bees. The proposed BeeAdhoc algorithm allows multiple communication paths between UAVs to be established in the first phase (reconnaissance phase) by broadcasting forward and backward scouts, including the source ID, hop count, and minimum remaining energy. In the second phase (resource collection phase), the UAV will collect information packets from the origin to the destination (Figure 14). When UAV (S) wants to communicate with UAV (D), it will do so by sending a \"scout\" through the network. The multiple routes found are used to send the reconnaissance mission back to UAV (S). The data are sent along the shortest path formed by high-energy UAVs. In [58], the authors proposed an improved artificial bee swarm routing algorithm, based on multi-strategy aggregation (IABC), to generate suitable paths for UAVs. The proposed IABC coding method is based on the definition of path nodes using cubic spline interpolation. It is applied to construct the collision-free fitness function of UAVs, which avoids the generation of non-flying paths. The IABC is based on a hybrid mechanism of chaotic mapping and the Pareto principle to initialize the population, enabling a full search of the solution space, and provide the approximate optimal flight path for UAVs.