## 6 Simulation Experiment
### Simulation Experiment on NPAM of UWSNs  
In order to verify the Networking Parameters Adjustment Method (NPAM) for optimized systematic performance of UWSNs, and referencing the parameter values in Figure 9b, this part considers setting the systematic performance values of the optimal goal as follows: \(\Theta_{0}=200\), \(\Phi_{0}=3\), \(\Psi_{0}=1\), \(\Omega_{0}=10\), so it realizes the optimal operation for Equation (21). All initial values of variables, the intermediate results and the optimized of parameters are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **Our work** & **Work [19]** & **Work [20]** & **Work [21]** & **Work [22]** & **Work [26]** & **Work [27]** \\ \hline Coverage & \(\surd\) & \(\surd\) & \(\surd\) & – & – & \(\surd\) & – \\ Connectivity & \(\surd\) & \(\surd\) & – & – & – & \(\surd\) & \(\surd\) \\ Durability & \(\surd\) & – & – & \(\surd\) & \(\surd\) & – & \(\surd\) \\ Rapid-reactivity & \(\surd\) & – & \(\surd\) & \(\surd\) & – & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: SQPCM compared with some other works.  
Figure 10: Relationship performances and parameters: (**a**) Coverage; (**b**) Connectivity; (**c**) Durability; (**d**) Rapid-reactivity.  
In the first round, we set \(f(m)=\sqrt{3}\), namely, the smallest ratio of the communication radius, to be the detection radius. The optimization results are shown in line 2 of Table 5. Because of the estimation method, \(k_{b}\) is higher than the actual value. Then the nonlinear optimization function makes \(c_{b}\), \(k_{b}\) be decimals. Actual values of these parameters can be obtained in the subsequent evaluation. After the first round, the network sets up the corresponding parameters according to the optimized adjustment scheme proposed in this paper, and we then evaluate and calculate the performance items according to the evaluation method. It is found that the actual value of the connection degree is 5, and the actual values of the performance items are \(\Theta_{1}=227\), \(\Phi_{1}=2.0148\), \(\Psi_{1}=1.1087\), \(\Omega_{1}=6.0261\), which cannot meet the requirements, so the optimization adjustment must continue.  
In the second round, we set \(f(m)=2\), namely, the second-smallest ratio of the communication radius, as detection radius. The optimization results are shown in line 3 of Table 5. The results of \(c_{b}\), \(k_{b}\) are still decimals and the optimization results are not accurate. However, after disseminating the communication radius and detection radius information to the network, exact values \(c_{b}=1\), \(k_{b}=9\) can be obtained. At this point, the systematic performance values actually meet the requirement, so it reaches the final state. Comparing the situation before and after optimization, the change of some networking parameters is shown as Figure 11.  
The experimental results show that by adjusting the networking parameters in our approach, the network systematic performance can achieve the mission objectives at minimal cost. Before deploying and running a network, the network can be optimized at any time according to the actual situation to change the performance of different dimensions, which provides a guarantee for the execution of actual missions.