## 4 Analysis and Perspectives
### Analysis of Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Network Scale (Sorties)** & **UAV Type** & **Method** & **Dataset Type** & **Simulation Platform** \\ \hline
[114] & 14–18 & Medium to large drones &
\begin{tabular}{c} Minimize network \\ power+K-means \\ \end{tabular} & Self-generated & MATLAB \\ \hline
[95] & 3 & Medium to large drones & Q-learning & Self-generated & MATLAB \\ \hline
[98] & 100–1000 & Medium to large drones & RL & Self-generated & MATLAB \\ \hline
[96] & 25 & Small drones & Q-learning & Self-generated & WSNet \\ \hline
[104] & 20, 40 & Small drones & Q-learning & Self-generated & NS-3 \\ \hline
[87] & 1–7 & Medium to large drones & SA+MILP & Real dataset & MATLAB+ILOG \\ \hline
[115] & 150 & Small drones &
\begin{tabular}{c} KF+Shortest-circuit \\ algorithm \\ \end{tabular} & Self-generated & MATLAB \\ \hline
[75] & 50–100 & Medium to large drones & OPAR & Self-generated & NS-3 \\ \hline
[116] & 10–100 & Small drones &
\begin{tabular}{c} Greedy \\ algorithm+HA+RL \\ \end{tabular} & Self-generated & OPNET \\ \hline
[117] & 5–100 & Medium to large drones & MILP+HA & Self-generated &
\begin{tabular}{c} Python+AWS \\ c5n.4 xlarge \\ \end{tabular} \\ \hline
[97] & 10–20 & Different types of drones & SA+Q-learning & Self-generated & N/A \\ \hline
[118] & 100 & Small drones & ACO & Real dataset &
\begin{tabular}{c} Raspberry \\ Pi+Python \\ \end{tabular} \\ \hline
[119] & 25 & Different types of drones & GA & Real dataset & MATLAB \\ \hline
[120] & 200 & Small drones &
\begin{tabular}{c} DNN+Greedy \\ algorithm \\ \end{tabular} & Self-generated & NS3 \\ \hline
[121] & 10-100 & Small drones &
\begin{tabular}{c} HA+Fuzzy \\ clustering \\ \end{tabular} & Self-generated & MATLAB \\ \hline
[122] & 10–40 & Small drones &
\begin{tabular}{c} Multicast routing \\ protocol algorithm \\ \end{tabular} & Self-generated & NS-2 \\ \hline
[123] & 40–100 & Small drones & DDQN & Self-generated & Python \\ \hline
[124] & 100 & Small drones & SA & Self-generated & Python+F-SDN \\ \hline
[85] & 20–200 & Small drones & PSO & Self-generated & MATLAB \\ \hline
[125] & 1–4 & Medium to large drones & LS+HA & Real dataset & MATLAB \\ \hline
[83] & 30 & N/A & ABC & Self-generated & N/A \\ \hline
[74] & 137 & Small drones &
\begin{tabular}{c} Online learning \\ algorithm \\ \end{tabular} & Real dataset & MATLAB \\ \hline
[86] & 15 & Small drones & PSO & Real dataset & N/A \\ \hline
[126] & 25 & Small drones & Q-learning & Self-generated & WSNet \\ \hline
[127] & 1–6 & Small drones &
\begin{tabular}{c} Agent-based \\ algorithms \\ \end{tabular} & Self-generated & Python+Windows \\ \hline
[89] & 5–30 & Small drones & ALNS+MBATA & Real dataset & Python+Gurobi \\ \hline
[128] & 10–100 & N/A & \
\begin{tabular}{c} Routing conver- \\ sign+Shortest path \\ algorithms \\ \end{tabular} & Self-generated &
\begin{tabular}{c} Python+Gurobi \\ +Windows \\ \end{tabular} \\ \hline
[129] & 20–40 & Small drones & L\&F (HA) & Self-generated &
\begin{tabular}{c} Java+Python \\ +CLPEX+ILOG \\ \end{tabular} \\ \hline
[130] & \textgreater{}100 & N/A & Hybrid algorithms & Self-generated & MATLAB+C++ \\ \hline
[131] & 10–40 & Small drones &
\begin{tabular}{c} Multicast routing \\ algorithms \\ \end{tabular} & Self-generated & NS-2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._We have focused mainly on the experimental methods, the objects, and the settings. The studies listed in Table 1 will be analyzed below. Research subjects: As shown in Figure 8, 60.4% of the routing algorithms we discuss are focused on small rotorcraft UAVs. The reason for this in our analysis is that because of their low manufacturing and development costs and their wider user-base orientation, the relevant UAV companies invest more in the development of small UAVs, which ultimately leads to smaller UAVs having an advantage in terms of maturity. However, in FANET construction, cooperative communication of UAV nodes at all levels is required.