Node Self-Deployment Algorithm Based on an Uneven Cluster with Radius Adjusting for Underwater Sensor Networks  
[PERSON]  
1 College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (F.W. )12 Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou 310018, China2  
[PERSON]  
1 College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; [EMAIL_ADDRESS] (Y.X. ); [EMAIL_ADDRESS] (F.W. )12 Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou 310018, China2  
[PERSON]  
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self-deployment [11; 12; 13; 14; 15; 16]. Static deployment assumes that nodes do not have the capability to move and need to use artificial methods to deploy [17]. It is usually a centralized algorithm, needing the _a priori_ environment information to calculate the pre-deployment positions; thus, it is inappropriate in many UWSN cases, such as underwater resource exploration and marine military defense [4]. Free-to-move node self-deployment assumes that nodes have the capability to move freely and can move in all directions [2]. Move-restricted node self-deployment assumes that nodes have the capability to move only vertically and can adjust their depth by themselves [18]. These two algorithms do not need any _a priori_ information, so they are applied more widely. However, the former, needing some autonomous underwater vehicles to assist during the deployment process, leads to high energy and resource costs, as well as high operation difficulty [19; 16; 20]. On the contrary, the latter, having developed a simple underwater sensor node with vertical mobility by the method of filling in or drawing off water, operates simply and availably [21; 22]. Thus, move-restricted node self-deployment is more practical than free-to-move node deployment. On the one hand, a number of related studies have been conducted on the method of move-restricted node self-deployment. [PERSON] _et al._[23] proposed a Voronoi-based depth-adjustment scheme for UWSNs. To maximize the network coverage rate, this scheme compares the Voronoi region area of every node in the same layer to determine the nodes that should go down to the next layer. This process continues layer by layer until the nodes in the last layer have been determined. However, this method uses a centralized manner to adjust node depth, which is difficult to achieve in practice. [PERSON] _et al._[24] proposed a distributed self-deployment depth-adjustment algorithm to adjust the depth of nodes after their initial deployment to reduce the coverage overlaps between two neighboring nodes. Nodes continue to adjust their depth until the sensor coverage can no longer be improved. [PERSON] _et al._[25] proposed a coverage algorithm based on fixed-directional movement for underwater sensor networks; this algorithm uses virtual forces to adjust a node to a position with zero joint force exertion and achieves node intelligent deployment in an independent manner. However, the aforementioned algorithms all view the network coverage rate as a standard and ignore the effect of the network connectivity rate on the network quality of service. [PERSON] _et al._[26] proposed the connected dominating set (CDS) based depth computation algorithm (CDA) on the basis of [24]. The algorithm initially constructs a connected backbone and then uses the dominator node on the backbone to individually optimize the position of its non-dominators iteratively. The algorithm ensures full-network connectivity while maximizing network coverage and reducing network deployment consumption. However, similar to other algorithms, CDA considers fixed communication radius nodes as deployment objects, which makes the nodes not be able to select an appropriate position flexibly during the process of node depth adjustment. CDA also views the network coverage rate or network connectivity rate as the criterion to deploy nodes and does not consider the number of nodes deployed within a certain range around the sink node.