## 5 Simulation and Performance Evaluation
### Algorithm Simulation and Analysis
This was mainly because the deployment strategy of this node was carried out for the condition of full connectivity, so the tabu table could record the distribution status of each node, thereby effectively preventing overlap between nodes. Therefore, the number of deployed nodes decreased under specific spatial conditions. By comparing Figure 6(a),b, we can see that the number of nodes required for all of the deployment methods increased as the number \begin{table}
\begin{tabular}{c c c c} \hline  & **Experiment 1** & **Experiment 2** & **Experiment 3** \\ \hline \(C_{pl}\) (m) & 243.92 & 280.69 & 360.41 \\ \(N_{dn}\) & 11 & 13 & 20 \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental statistical results. Figure 6: Experiment 3: 30 events that were randomly distributed (unit: m). of events increased. This was because more events required more nodes to cover them. In addition, when the node sensing space covered the entire monitoring region, as the number of events increased, the number of nodes remained essentially unchanged, as shown in Figure 7b. Figure 8a,b shows the comparison of the distribution density of the deployed nodes for different numbers of events. The three algorithms can achieve good results in network coverage, but there are many differences in node distribution density. The events in the grid were randomly distributed, causing the node deployment to not only cover events, but also to ensure consistency between the node distribution density and the event distribution density as much as possible [16]. Based on the statistical methods for node distribution density proposed by [PERSON] et al. [16], we compared the fit between the node distribution density and the event distribution density of the three algorithms. From the figure, we can see that when the three algorithms were compared, the MCC-ACO algorithm proposed in this paper can achieve high coverage performance, which was generally maintained at above 0.9. This is mainly because the NDSBA and ACO-MCC3D algorithms tended to obtain local optimal solutions and their nodes tended to move blindly. These algorithms do not consider the fit between the node distribution and the event distribution. However, the MCC-ACO algorithm utilizes the characteristic of network full connectivity to record the distribution status of all of the nodes in the network and establish a tabu table so that overcrowding in certain sites is avoided during the movement of nodes, thereby avoiding multiple coverage of events. In addition, from Figure 8a,b, we can see that when the node sensing radius \(r^{s}\) and the communication radius \(r^{c}\) are smaller and when the number of events is lower, there is no need for an excess number of nodes to cover events. However, more nodes are required to maintain the communication between nodes and the sink. Therefore, the coverage performance of the event set is smaller. As shown in Figure 8a, as the number of events increases, the fit relationship becomes better, and this relationship maintains a relatively high value, as shown in Figure 8b. Figure 7: Comparison of the number of deployed nodes for different numbers of events.