## 5 Simulation and Performance Evaluation
### Algorithm Simulation and Analysis  
We conducted three sets of experiments in a \(50\) m \(\times\)\(50\) m \(\times\)\(50\) m three-dimensional underwater monitoring region. For Experiment 1, 10 events were randomly distributed. For Experiment 2, 20 events were randomly distributed. For Experiment 3, 30 events were randomly distributed. It should be noted that the interval was set as 10 m, i.e., there were \(6\times 6\times 6\) grid points in the three-dimensional underwater monitoring region. We employed the MCC-ACO algorithm to achieve the deployment of the sensor nodes. The operation results are shown in Figures 4-6. The red stars represent events, and the blue dots represent the locations of the nodes (the sphere is the three-dimensional sensing range). It can be seen that the MCC-ACO algorithm can achieve event coverage with all of the nodes, and the deployed nodes can all connect to the sink. More importantly, the node distribution density and the event distribution density achieved a good fit. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Scale** & **Number** & **Std.** & **Time** & **Std.** & **Scale** & **Number** & **Std.** & **Time** & **Std.** \\ \hline MCC-ACO & \(5\times 5\times 5\) & 17 & 1.68 & 8.08 & 1.25 & \(8\times 8\times 8\) & 15 & 1.89 & 14.17 & 3.19 \\ Standard ACO & \(5\times 5\times 5\) & 81 & 5.32 & 1.76 & 0.13 & \(8\times 8\times 8\) & 324 & 23.68 & 11.95 & 1.23 \\ MCC-ACO & \(6\times 6\times 6\) & 17 & 4.52 & 9.14 & 3.25 & \(9\times 9\times 9\) & 15 & 1.79 & 18.16 & 3.38 \\ Standard ACO & \(6\times 6\times 6\) & 144 & 13.57 & 3.09 & 0.33 & \(9\times 9\times 9\) & 495 & 29.76 & 26.48 & 2.35 \\ MCC-ACO & \(7\times 7\times 7\) & 18 & 4.32 & 14.98 & 3.03 & \(10\times 10\times 10\) & 15 & 1.89 & 14.17 & 3.19 \\ Standard ACO & \(7\times 7\times 7\) & 232 & 13.15 & 6.57 & 0.45 & \(10\times 10\times 10\) & 604 & 38.59 & 42.99 & 3.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistic results of the experiments. Figure 4: Experiment 1: 10 events that were randomly distributed (unit: m). Figure 5: Experiment 2: 20 events that were randomly distributed (unit: m). In addition, Table 2 shows the statistical results of the aforementioned experiments in detail. \(C_{pl}\) represents the length of the communication path, and \(N_{dn}\) represents the number of deployed nodes. In order to eliminate the random effects in the experiments, the mean of 20 experiments was taken for every set of experiments, and the number of deployed nodes and the lengths of communication paths were recorded. It is clear that as the number of events increased, the number of nodes to be deployed increased correspondingly. In addition, the distances of the data transmission paths also increased. This was because the events were randomly distributed and relatively scattered. Hence, some nodes were needed to cover events, while other nodes were needed to maintain the connection between the nodes and the sink even though they did not cover events. In the following section, this paper divides the 50 m \(\times\) 50 m \(\times\) 50 m three-dimensional underwater monitoring region into 25 \(\times\) 25 \(\times\) 25 grid points. The performance of the three algorithms is compared when the sensing radius \(r^{s}\) and communication radius \(r^{c}\) are 10 m and 20 m, respectively. Figure 6(a),b shows a comparison of the number of deployed nodes for different numbers of events. It can be seen that when the number of events was the same, the number of nodes that were required for deployment in the MCC-ACO and ACO-MCC3D algorithms was relatively lower than the number of nodes that was required for the NDSBA algorithm. This was because these two deployment strategies improved on the heuristic search rules. Consequently, further grid points were preferentially selected, producing a sparse deployment network and reducing the total number of deployed nodes. In addition, compared with the ACO-MCC3D algorithm, the algorithm proposed in this paper required a lower number of deployed nodes.