## 1 Introduction
As discussed, this study reviews the research on coverage of wireless sensor networks in 2D and 3D environments using intelligent optimization algorithms and compares coverage algorithms in the literature. The results are shown in Table 1.  
Given the non-universal seawater environment, nodes in the underwater environment are generally difficult to replace; their power supplies are also generally difficult to charge. The energy problem directly affects the service life of the entire sensor network. At present, many reliable and energy-saving underwater communication routing protocols are applied to solve energy loss by UWSNs. [PERSON] et al. designed a routing protocol with the help of vector position information. By obtaining node coordinate information, packets are forwarded in the virtual channel established between the sending node and the receiving node. However, when the distribution of nodes is sparse, there may be a next receiving node in the pipeline that cannot meet the forwarding conditions, leading to an interruption of the communication path [27]. In response to this problem, [PERSON] et al. put forward an improved HH-VBF scheme, which re-establishes a vector pointing to the destination node \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Surveyed Works** & **Proposed Method** & **Solved Problem** & **Advantages** \\ \hline [PERSON], [PERSON], 2019 [10] & CHHA & Coverage holes & Apply virtual force under Delaunay triangulation. \\ [PERSON], 2022 [15] & VF-IMFO & Coverage holes & Analyze virtual force for node path optimization. \\ [PERSON], 2019 [16] & VBO & Energy consumption & Multi-energy optimization during redeployment. \\ [PERSON], 2023 [17] & IGS & Node coverage & Transform coverage problem into multiple local optimal. \\ [PERSON], 2017 [18] & UFOA & Node coverage & Optimal coverage under drosophila foraging behavior. \\ [PERSON], 2018 [21] & VFRBEC & Node coverage & Correct node displacement underwater flow force. \\ [PERSON], 2019 [23] & k-ERVFA & \(k\)-coverage & An uneven coverage for \(k\)-coverage requirements. \\ [PERSON], 2019 [24] & DABVF & Node coverage & Node virtual force and fault judgment mechanism. \\ [PERSON], 2022 [26] & VF-PSO & Node coverage & Optimize network coverage and distance threshold. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of coverage optimization algorithms. on each hop forwarding node, calculates its distance to the vector hop by hop, and compares it with a specified threshold to determine whether to forward the packet. The strategy of changing the routing vector by hop number alleviates the problem of unbalanced energy consumption of network nodes to some extent [28]. However, since a routing pipeline is still used, it is still possible that the next hop node does not exist. Ref. [29] used depth information as a condition for routing and forwarding. However, since node depth is the only forwarding parameter, nodes with smaller depths will forward packets most of the time and their energy will soon run out, causing them to die. To address the energy loss by DBR, an energy-saving protocol was designed [30]. This protocol takes energy as one of the conditions of routing and forwarding to extend the running cycle of the network. All the methods discussed above can reduce network energy loss, but they ignore the distribution position of nodes in actual situations, resulting in network communication interruptions, unbalanced energy consumption in the process of data forwarding, and waste of node resources. In summary, unlike the modeling environment on the ground, the underwater environment is an open dynamic area. There are many difficulties in constructing the UWSNs due to the undulating terrain of the seabed and the hydraulic effects of water flow.