## 7 Conclusions  
This study addressed the problems of node depth-adjustment deployment (vertical movement deployment) for UWSNs. We have proposed NDACS, that forms a connected 2D convex hull spanning tree first and then connects the nodes of a spanning tree to calculate the depth-adjustment distances of their neighbor nodes based on time marker while maintaining the connectivity between the parent node  
Figure 11: Comparison of energy consumption between NDACS and VBDA: (**a**) comparison of node average energy consumption of communication between NDACS and VBDA varying with the number of nodes, and the initial communication radius is 60 m; (**b**) comparison of node average energy consumption of movement between NDACS and VBDA varying with the number of nodes. Figure 12: Comparison of average node degree between NDACS and VBDA. and their neighbor child nodes through the introduction of the mechanism of adjustable communication radius model, until the node deploys to the depth threshold and achieves the overall network coverage deployment. Through simulation experiments, we have evaluated the different indicators including network connectivity rate and network coverage rate, energy consumption of communication and movement, and network stability. The comparison of these simulation results shows that the various performances of the NDACS algorithm are better than those of VBDA. The symbolic notation of the NDACS algorithm is described in Table 3.  
In the future, we plan to study the efficiency of network data acquisition when achieving network deployment by designing a more proper routing strategy for this deployment algorithm scenario. In real-world network systems, usually the model of communication and sensing is based on probability. The probability model of communication and sensing is worthwhile to adopt on simulation setup, then executes comparative analysis on experiment results. Meanwhile, the redeployment of the set of nodes \(NeigInter_{i}^{i+1}\) is also worthy of further study. Furthermore, adding the obstacle model or water mobility model to increase the practical applicability of the algorithm is also included in our future research work. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(N\) & Number of nodes \\ \(ID\) & Node unique identifier \\ \(Rs\) & Node sensing radius \\ \(Rc\) & Node communication radius \\ \(Rc_{init}\) & Initial communication radius \\ \(\theta\) & Adjustable level of communication radius \\ \(B_{u}\) & Accumulation of communication radius \\ \(S_{i}\) & ID is \(i\) of node \\ \(D\) & Transmitting distance of the information package \\ \(P_{0}\) & Power threshold of packets can be received \\ \(F\) & Carrier frequency \\ \(T_{p}\) & Transmission delay of data transmission \\ \(K\) & Energy spreading factor \\ \(M_{e}\) & Movement energy consumption of node \\ \(m_{d}\) & Node movement distance \\ \(m_{u}\) & Energy consumption on unit moving distance \\ \(Ss\) & Set of sleep nodes \\ \(Sa\) & Set of active nodes \\ \(sleep\_in\) & Set of sleep nodes inside the convex hull \\ \(sleep\_out\) & Set of sleep nodes outside the convex hull \\ \(S_{c}\) & Network coverage area on process of convex hull activation \\ \(S_{CH}\) & Area of 2d convex hull \\ \(S_{Hole}\) & Area of coverage holes \\ \(Rh\) & Empty circle radius \\ \(N_{A}\) & Total number of active nodes \\ \(NeI\_A\) & Active neighbor nodes set \\ \(Neif(_{j}^{i})\) & Neighbor nodes set of node \(f_{j}^{i}\) \\ \(NeigInter_{i}^{i+1}\) & Insertion of neighbor sleep nodes \\ \(\{f_{j}^{i}\}\) & Parent node set of subtree \\ \(T_{mark}\) & Time marker of active node \\ \(d_{th}\) & Depth-adjustment threshold of network \\ \(C_{th}\) & Coverage threshold on 2d network surface \\ \(ID_{A}\) & Activation identifier of node \\ \(C_{c}\) & Network coverage rate \\ \(C_{n}\) & Network connectivity rate \\ \(A\) & Ratio of node communication radius to sensing radius \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main Symbolic Notation of the NDACS Algorithm.