## 2 Related Work  
Some scholars have recently developed free-to-move node self-deployment algorithms for UWSNs. [PERSON] et al. [20; 21] proposed three kinds of movement-assisted sensor deployment algorithms, namely, vector-based algorithm, Voronoi-based algorithm, and Minimax algorithm. These three algorithms are all based on the Voronoi diagram. In the vector-based algorithm, a node moves to the next position according to the repulsive force exerted on the node by its Voronoi neighbor nodes. In the Voronoi-based algorithm, a node moves to the farthest vertex of its Voronoi diagram every round. In the Minimax algorithm, a node moves closer to the farthest vertex of its Voronoi diagram to avoid the situation in which a vertex that was originally close becomes the new farthest vertex. In these algorithms, each node can move autonomously to the corresponding position to cover the coverage hole according to its own local information. Thus, these algorithms can increase network coverage, and meet the distributed characteristic of the sensor network. However, the Voronoi diagram of each node should be formed in each of the iterations, and this process requires each node to know the global information of the network and consequently results in extra communication energy consumption. Moreover, the process based on the 3D Voronoi diagram of the node is more complex compared with that based on 2D Voronoi diagram. Thus, these algorithms are suitable for 2D node self-deployment but unsuitable for node self-deployment in UWSNs. [PERSON] et al. [22] proposed a sensor deployment algorithm based on self-organizing maps for the event. In each of the iterations, an event is selected by a random sequence, and the node that is closest to the event should update its position according to the rule of movement. The node moves a certain distance to the event, and the distance of the movement is proportional to the distance between the node and the event. The algorithm is iterated until no more significant movement is possible. This algorithm can deploy the node according to the density of the event in the region and accomplish an uneven deployment in the target area. However, this algorithm adjusts the node position by the centralized way, which is difficult to achieve in practice. [PERSON] et al. [23] proposed a fish swarm-inspired underwater sensor deployment algorithm (FSSDA). Drawing on the foraging behavior of the fish swarm, this algorithm regards the node as the fish and the event as the food, moves the node to cover the event, and finally completes the deployment for the event according to the crowding parameter of each event. The algorithm has low computational complexity and rapid convergence rate. [PERSON] et al. [24] proposed a particle swarm-inspired underwater sensor self-deployment algorithm. The process of the node deployment in the algorithm is similar to that in FSSDA, but the algorithm also considers the situation in which the position of the event is dynamically changing, like the situation in practice. However, both algorithms are suitable for the distributed coverage of the event but unsuitable for the coverage of the region. In addition, these algorithms also ignore network connectivity. [PERSON] et al. [25] proposed a three-dimensional virtual forces deployment algorithm (TVFDA). This algorithm applies the concepts of gravitation and repulsion in physics to the wireless sensor network and considers that these forces exist between nodes in the network. Therefore, in the algorithm, each node moves to the next position according to the join forces, which are exerted on the node by its neighbor nodes, and each node stops moving until its join forces is zero or the maximum iteration number is reached. This algorithm allows for an even distribution of the nodes in the target area compared with situation before the algorithm is run; thus, this algorithm enhances network coverage and meets the characteristics of underwater wireless sensor networks.