# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 6 Simulations
It should be noted that different node numbers have an effect on tracking performance. With more nodes, we can get more information and achieve a better tracking performance, but it may lead to higher energy consumption. How to find a balanced method between energy consumption and tracking error via different node number is an important problem. However, it is not the main concern in this paper; thus, we always wake up three nodes during the tracking process. We will study the adaptive method to determine node number in future work. In addition, our method is not limited to  
Figure 3: The tracking error comparison between MOVE and STATIC with different moving depths. (**a**) tracking errors for MOVE and STATIC with moving depth \(h=-80\) (over 100 MC runs); (**b**) tracking errors for MOVE and STATIC with moving depth \(h=-100\) (over 100 MC runs); (**c**) tracking errors for MOVE and STATIC with moving depth \(h=-120\) (over 100 MC runs); (**d**) average tracking errors for MOVE and STATIC with different moving depths (over 100 MC runs). the water surface network. The reason we use such a network is that it is more practical and cheaper than 3D networks. Then, we compare the effect with different node density in UWSNs. The simulation results of MOVE and STATIC with node distance \(dis=\{90,150,180\}\) are plotted in Figure 4 (results of \(dis=120\) have been presented in Figure 3c). It is not surprising that the MOVE scheme always performs better than STATIC. As shown in Figure 3d and Table 2, our MOVE scheme can maintain good performance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Schemes** & \(\mathbf{h=-120}\) & \(\mathbf{h=-100}\) & \(\mathbf{h=-80}\) & \(\mathbf{h=-60}\) \\ \hline STATIC & 7.807 & 7.271 & 6.977 & 6.415 \\ MOVE & 6.252 & 6.187 & 6.209 & 6.156 \\ Improvement & 19.92\% & 14.91\% & 11.01\% & 4.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average tracking errors with different moving depth \(h\). Figure 4: The tracking error comparison between MOVE and STATIC with different adjacent node distance. (**a**) tracking errors for MOVE and STATIC with adjacent node distance \(dis=90\) (over 100 MC runs); (**b**) tracking errors for MOVE and STATIC with adjacent node distance \(dis=150\) (over 100 MC runs); (**c**) tracking errors for MOVE and STATIC with adjacent node distance \(dis=180\) (over 100 MC runs); (**d**) average tracking errors for MOVE and STATIC with different adjacent node distance (over 100 MC runs). no matter whether UWSNs are sparse or dense due to the fact that it can adjust nodes to the optimal depth at all times. On the contrary, the node density has a big effect on tracking accuracy of the STATIC scheme. The reason is that when nodes are sparsely deployed in the network, the activated nodes are far from each other, and they can measure the targets in different positions and angles. However, in densely deployed networks, the activated nodes are close to each other. Thus, a lot of information provided by their measurements is overlapping. As shown in the results, our MOVE scheme is a good solution to this problem. Furthermore, the searching performance of our improved HS algorithm is presented in Figure 5. We use the convergence process of objective function value to show the searching performance of the optimization method. In addition, the convergence process of traditional HS algorithms is presented as a comparison. In this simulation, we show the 500 iterations' convergence process of objective function value via two methods at the same time interval \(k=25\), and the results are the averaged value after 100 Monte Carlo simulations. It is obvious that our improved HS algorithm can achieve faster searching speed and better convergence value. For our improved method, objective value converges to \(-0.9818\), while the value with the traditional method only converges to \(-0.9769\). In addition, our improved HS algorithm needs less iteration to achieve the same objective value, and can achieve a better value finally.