## 5 Performance Analysis
### Sensor Deployment in a Dynamic Environment  
To analyze the reliability and adaptability of the DHFSOA algorithm, this section explores the results of sensor deployment in a non-uniformly covered, dynamic ocean environment. Water flow velocity was generated based on a model presented in a previous study [19; 24]; model parameters are listed in Table 2. The update period \(T\) for sensors in the DHFSOA was 0.5 s.  
For the case in which events take place in a dynamic ocean environment, flowing water will cause their positions to change. The simulation results at four different times are shown in Figure 8a-d. As can be seen in the figures, when events present a linear distribution, underwater nodes also exhibit a linear distribution, and regions with high event densities have more underwater nodes. It can be seen that underwater nodes move with events and always present the same distribution shape. The node covers the events well, and achieves the matching of underwater node density and event density.  
Figure 7: Comparison of the evolution of total moving distance and event coverage of two methods in three sets of experiments. (**a**) Experiment 1: average coverage; (**b**) Experiment 1: total moving distance of nodes; (**c**) Experiment 2: average coverage; (**d**) Experiment 2: total moving distance of nodes; (**e**) Experiment 3: average coverage; (**f**) Experiment 3: total moving distance of nodes.  
Next, the network operation time was divided into 10 segments. Figures 9 and 10 respectively compare the coverage efficacy of the event set and the evolution of the total moving distance of the nodes during each monitoring period. It can be seen in Figure 8 that the coverage efficacy of the event set is constantly changing with time, and both the DHFSOA and PSSD algorithms maintain good states. The DHFSOA, however, dynamically adjusts quickly and is slightly better than the PSSD algorithm. Figure 9 is a comparison diagram between the PSSD algorithm and the DHFSOA for the changes in total node moving distance during the network running time. It can be seen that, compared with the PSSD, the DHFSOA algorithm greatly reduces the total moving distance of the nodes during the network operation, thus reducing total energy consumption. This allows the nodes to retain more energy, which can be used to participate in other tasks, effectively extending the network life cycle.  
Figure 8: The distribution of sensor nodes and events at times \(t_{1}\) to \(t_{4}\). (**a**) initial time node \(t_{1}\) and event distribution; (**b**) initial time node \(t_{2}\) and event distribution; (**c**) initial time node \(t_{3}\) and event distribution; (**d**) initial time node \(t_{4}\) and event distribution.  
We can see from the preceding figures that, compared to the PSSD algorithm, the DHFSOA has obvious advantages in terms of network coverage, coverage efficacy of event sets, and total moving distance of nodes. This is due to that fact that, during network operation, the DHFSOA constructs an information pool, expands the nodes' field of vision, enhances information sharing between nodes in the network connectivity state, avoids blind movement of nodes, and retains the global search ability of the traditional fish swarm heuristic algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**The Water Flow Field**} & \multicolumn{4}{c}{**Target Number**} \\ \cline{2-7}  & \(\mathbf{k}\) & \(\mathbf{c}\) & \(\mathbf{av}\) & \(\mathbf{\epsilon}\) & \(\mathbf{\omega}\) & **Sensors** & **Events** \\ \hline Value & \(\frac{2\pi}{75}\) & 0.12 & 1.2 & 0.3 & 0.4 & 6 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the dynamic ocean environment.  
Figure 10: Comparison of the evolution of the total moving distance of nodes at different times.  
Figure 9: Comparison of the evolution of coverage efficacy at different times.