## 3 System Model  
In AUSCNs, there is one sensor node on each drone, and communication between multiple UAVs is carried out through sensors on the UAV. AUSCNs have the characteristics of a large network scale, many nodes, a long monitoring cycle and a relatively large amount of data, so time-driven data acquisition is adopted. For the sake of preventing the effects of other factors, a model of the network energy consumption is adopted in this paper. The following assumptions are made for the model:  
1. UAVs are randomly distributed in an AreaL \(\times\) AreaL monitoring area. Each UAV is completely isomorphic and represents a sensor node. Each node has the same initial energy and has a unique identifier. ;
2. After the deployment of sensor nodes, the monitoring area will be stationary;
3. The base station is unique and its location does not change. Its resources are very sufficient, which can completely ignore the problem of energy consumption, and has a supercomputing and processing capacity;
4. All sensor nodes can acquire their own position information, and wireless communication can be carried out directly between different nodes and base stations; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Years** & **Proposed Method** & **Optimization Strategy** & **Limitations** \\ \hline [PERSON] et al. [28] & 2022 & CHSW & Integrated model based on intelligence and mechanism application, optimized in three stages. & When faced with large-scale drones, it is impossible to find a suitable cluster head. \\ \hline [PERSON], [PERSON] et al. [29] & 2022 & MOEA/D (DSDA-MOEA/D) & In the genetic algorithm, the feasibility check operator of binary search is introduced to improve performance, utilizing the MOEA/D framework and neighboring subproblem search. \\ \hline [PERSON] et al. [30] & 2022 & CUCO, DEA, HBA & Three different metaheuristic optimization algorithms were used to obtain the relationships between parameters based on Markov chain analysis studies. & Since the algorithm parameters of this method are fixed, it is easy to fall into local optimality and fail to obtain the optimal solution. \\ \hline [PERSON] et al. [31] & 2023 & SBA & Leverage SBA to solve the drone placement problem through parallelization. & That method can easily fall into evolutionary stagnation and fail to obtain a coverage solution that satisfies specific scenarios. \\ \hline [PERSON]. [PERSON] et al. [33] & 2021 & HAECR & Adopt a hierarchical adaptive strategy. & The algorithm converges too slowly and does not take into account complex underwater biological factors. \\ \hline This paper & 2023 & NAEEA & Improve the performance of the NAEEA through newly designed niche strategies, adaptive strategies and elitist strategies. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing UAV network optimization methods. After data fusion, the frequency of the cluster head node sending data to the network nodes is low, and the amount of data is small. Thus, the process of data transmission between the cluster head nodes and the gateway node is not considered in the model. The network clustering structure of AUSCNs is shown in Figure 1.  
Figure 1 depicts the network clustering arrangement within AUSCNs, revealing a total of five cluster groups. Within each cluster group, the cluster head node is represented by the color blue, while the member node is denoted through the color yellow. The uplink transmission steps are as follows: Firstly, the sensing nodes representing each UAV are grouped into different clusters, with a head node in each cluster. Then, sensing nodes randomly scattered in the simulation range and centralize the sensing results to the head nodes. Next, the head nodes collect the data from the sensing nodes, and transfer the data to the gateway node through multi-hop or direct methods. Finally, the gateway node summarizes the data transmitted from each cluster head node; then, the gateway node delivers data to the user for deeper processing.