## 5 Simulation and Discussion  
In this section, in order to verify the performance of the algorithm, data analysis is conducted on the DJI Tello UAV, and the performance of the NAEEA algorithm in solving AUSCN clustering optimization is tested through simulation experiments. The DJI Tello drone is a programmable drone with a body diameter of 17 cm and a body weight of 87 g. It can shoot 5-million-pixel photos and 720p high-definition videos, and the maximum video transmission speed is 30 FPS. Specifically, the parameter information is shown in Table 4. In the same experimental environment, the simulation results of the NAEEA are compared with the SFLA, SA and EA. Unless otherwise specified, the simulation results  
Figure 9: Niche operation procedure. are the average of 10 running results. The experiments were conducted using MATLAB 2021a software on a computer equipped with Intel(r) Core(TM) i5-8300H CPU, 8 GB RAM and the Windows 11 operating system. In this experiment, in order to show that the NAEEA has a stable optimization effect in different scenarios, the simulation experiment will be carried out by setting a number of UAVs of different sizes and different cluster head ratios, as well as different monitoring area sizes, to test the proposed algorithm. The performance is tested; we conduct simulation experiments according to the IEEE 802.15.4 communication standard [36]. Table 5 shows the detailed data of the AUSCN clustering model in the experimental environment. In the simulation experiment, the electron energy parameter is 50 nJ/bit, the power amplification parameter is 100 pJ/bit/m\({}^{2}\), the data volume is 1 K/bits, the communication environment parameter is 3 and the cluster head ratio is 0.05. In addition, the population size of the NAEEA is 40, the mutation probability is 0.05, the crossover probability is 0.8, the niche grouping is two and the number of iterations is 100. In the EA, the mutation probability is 0.05 and the crossover probability is 0.8. In the SA, the initial temperature is 1000 \({}^{\circ}\)C, the temperature coefficient is 0.9, the Markov chain length is 100 and the number of iterations at the same temperature is 40. In the SFLA, the number of frogs is 40, the number of groups is 4, the number of iterations is 100 and the number of iterations within a group is 10. All algorithms stop running after 100 iterations, and the specific parameters are shown in Table 6.  
Table 7 shows the experimental parameters of Figure 10, increasing the number of UAVs while fixing the cluster head ratio and monitoring range. According to the experimental parameters in Table 7, Figure 10 shows the relationship between the network energy consumption and the number of iterations for the NAEEA, EA, SA and SFLA, respectively. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Type & DJI Tello \\ Body diameter & 17 cm \\ Body weight & 87 g \\ Maximum flight speed & 28.8 km/h \\ Flight duration & 13 min \\ Maximum video transfer speed & 30 FPS \\ Transmission distance & 0.1 km \\ \hline \end{tabular}
\end{table} 
Table 4: Detailed parameters of the Tello UAV. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline MAC protocol & IEEE 802.15.4 \\ Monitoring range & 100 \(\times\) 100 m\({}^{2}\) \\ Electronic energy parameter & 50 nJ/bit \\ Power amplification parameter & 100 pJ/bit/m\({}^{2}\) \\ Volume of data & 1 K bits \\ Communication environment parameter & 3 \\ Cluster head ratio & 0.05 \\ \hline \end{tabular}
\end{table} 
Table 5: Simulation parameters of AUSCNs.