## 2 Related Works  
In addition to a number of different strategies for node deployment of UWSNs, many researchers have focused on static deployment [16; 17; 18] and free movement deployment [19; 20; 21]. The static deployment algorithm refers to the assumption that nodes do not have the ability to move. The nodes need artificial deployment to a fixed location determined by advance calculation, and when the network deployment is completed, besides, the nodes no longer move. [PERSON] et al. [16] used different polyhedrons to fill three-dimensional (3D) deployment space and study the maximization of 3D coverage space with a minimum number of nodes, offering mathematical proof and derivation to explain why truncated octahedral cells result in the best strategy and achieve the greatest network coverage performance. Subsequently, basing on the study of [16], [PERSON] et al. [17] used space-filling truncated polyhedrons to execute network coverage deployment by controlling the cell-vertex nodes' active or sleep states, and adjusting the space-filling polyhedron radius can be flexible to achieve \(k\)-coverage. [PERSON] et al. [18] proposed a geometric hierarchical clustered deployment strategy where the cluster head nodes are alternated by using the geometric characteristics of the cluster shape and constraint condition that ensures network connectivity and full coverage. The optimal network lifetime mathematical model and cluster shape model are derived, and optimal node deployment is achieved. On the whole, the node goal location has been determined before the real deployment. Therefore, the static deployment algorithm usually can achieve good network coverage or connectivity performance. However, the node needs to be fixed to the definite position through artificial means, and obviously, the difficulty of deployment is excessively large and the deployment cost is excessively high for aquatic environments. In addition, when the network scale is expanded or topological changes occur because of the effect of water environment (water flow or animal collision), the applicability of the static deployment algorithm decreases rapidly. Free movement deployment refers to the assumption that nodes have the ability to move freely in each direction and can be moved to any location in the 3D network space. Considering the covering of the uneven isolated incident in the monitoring area, [PERSON] et al. [19] first proposed fish swarm-inspired node deployment and subsequently [20] proposed a similar algorithm--the particle swarm-inspired node deployment. These two algorithms simulate the behavior of fish swarms or particle swarms and introduce the model of congestion degree control. The proposed algorithms can drive nodes to cover the events and match event distribution density in the water environment. The author only considers the coverage rate of the events and ignores the network connectivity rate during the deployment process. [PERSON] et al. [21] proposed the 3D virtual forces deployment algorithm. This algorithm uses virtual force to adjust node location and improve network coverage rates and connectivity rates. However, this algorithm could not achieve full network connectivity, that is, the network connectivity rate is 1. For this kind of free movement, in all of the abovementioned proposed deployment algorithms, the node has the ability to move in each direction and can move to an optimized location where network coverage and connectivity performance are obviously improved. However, free movement deployment needs to equip these nodes with autonomous underwater vehicle (AUV) or other mobile devices under normal circumstances then its strategy induces relatively high production costs, which make the node free movement deployment algorithm difficult to spread in the practical UWSNs application. As a research team which has remained focused on WSNs, our team also proposed some node deployment algorithms for UWSNs in recent years [25; 26; 27; 28].