Those two increase network energy consumption and decrease network performance. In addition, the algorithm (_i.e._, CDA) considering both the network coverage rate and the network connectivity rate, only considers the redundant coverage rate of the location where the distance between the node and its next-hop node is \(R_{c}\) during the process of optimizing the node position; thus, the network coverage rate can still be improved. Furthermore, the energy consumption balance of the network formed by CDA performs poorly when the number of nodes is relatively sparse. On the other hand, some studies [27; 28] have also investigated network cluster formation in wireless sensor networks (WSNs). [PERSON] _et al._[29] proposed the low-energy adaptive clustering hierarchy (LEACH) algorithm. This algorithm selects a cluster head node randomly and completes clustering with the principle of the nearest neighbor. Its operation is simple, but it cannot control the distribution of the cluster head node effectively. [PERSON] _et al._[30] proposed an energy-efficient clustering scheme (EECS), which selects a cluster head node according to the residual energy of the node and then finishes the network cluster. Compared to LEACH, EECS selects the nodes with more residual energy preferentially as cluster head nodes, which reduces the exchange frequency of the cluster head node and enhances the network stability. [PERSON] [31] proposed a coverage-preserving routing protocol for WSNs. It selects the cluster head node based on the coverage redundancy rate of the node and then finishes the network cluster. Consequently, the network operates based on the cluster, and the great network coverage rate is maintained. In the method, a node having a greater coverage redundancy rate has priority to become a cluster head node, which reduces the node failure impact on the network coverage rate and slows down the drop speed of the network coverage rate. [PERSON] _et al._[32] surveyed different cluster formation algorithms, analyzing their objects, features, _etc._ [PERSON] _et al._[33] proposed an algorithm that can structure the topology of different WSNs to coexist in the same environment based on network clustering, where cluster head nodes manage their own networks and have connections with other cluster head nodes, resulting in good network connectivity and scalability of the whole parallel network structure. [PERSON] _et al._[34] proposed an uneven cluster mechanism. The mechanism divides the network into many clusters with different sizes. Among them, a cluster, further from the sink node, has a greater size. On the contrary, a cluster, closer to the sink node has a smaller size. The cluster distribution may balance the network energy consumption and extend the network lifetime. [PERSON] _et al._[35] proposed a chain structure-based uneven cluster routing algorithm. It establishes a dynamic multiple-hop route considering the uneven cluster mechanism for transmitting data, which can balance the energy consumption of nodes and prolong the network lifetime. To solve the aforementioned problems on move-restricted node self-deployment and according to the aforementioned cluster formulation algorithm description, this study proposes the uneven cluster and radius-adjusting self-deployment algorithm (URSA). After nodes are scattered on the water surface randomly and uniformly, each node begins the uneven clustering process according to the distance to the sink node. The cluster nodes then use the hybrid radius path-selection method simultaneously to form a connected path to the sink node. In the depth-adjustment phase, the cluster head nodes adjust their own depths according to the principle of maintaining the layout formed on the water surface by the uneven clustering process.