# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 2 Related Works
However, the effect of uneven node distribution on the selection of cluster heads is not considered. In [19], the proposed clustering algorithm (EGRCs) for data transmission classifies the whole 3D network into multiple grids, with each grid forming a cluster. The node with the largest residual energy and the shortest distance to the sink is selected as the cluster head. The next hop cluster head node is chosen according to the residual energy, end-to-end delay, and node-to-sink distance. This algorithm can prolong the network lifetime and improve the efficiency of energy usage, but it also fails to consider the impact of uneven node distribution on the network survival time based on grid clustering. In [20], another clustering protocol scheme is applied to multi-hop routing for UWSNs, which selects cluster heads by considering the node coverage redundancy. It is a metric that characterizes the extent to which the sensory area of a node is covered by neighboring nodes. The greater the coverage redundancy, the smaller the impact on the network coverage upon the death of a node. The distributed network unevenly layered coverage preserving routing algorithm (NULCPR) effectively reduces the impact of cluster head mortality on network coverage for each cluster. In [21], an energy-balanced unequal hierarchical clustering (EULC) algorithm is designed for UWSNs to reduce the energy consumption of communication between clusters. EULC generates clusters of different sizes in the same layer to address the \"hot zone\" problem. Based on the communication radius, the depth of network deployment is classified into multiple layers. The sensor node calculates the weight regarding the node degree, the distance to the sink node, and the residual energy of the node. The node with the highest weight is selected as the cluster head. Nodes store information about neighboring cluster heads and then calculate the routing function of neighboring cluster heads based on the distance between the residual energy and the node. Moreover, nodes select the next hop cluster head with the minimum routing function value. This algorithm considers the uneven distribution of nodes at each layer but does not consider the node distribution of the whole network and the effect of the coverage redundancy of cluster heads on the network coverage. In [22], a hierarchical classification-based clustering algorithm (UCUBG) is proposed to select cluster heads according to the density and residual energy of nodes. Then, the cluster head hierarchy is classified based on the location relationship between cluster heads. Finally, nodes select the cluster head with the lowest load into the cluster. This algorithm ignores the influence of cluster head coverage redundancy on network coverage, and only considers the number of neighboring nodes in a certain area when selecting cluster heads, without considering the influence of the distance between the node and its neighboring nodes on the node density. In [23], the authors implemented a hierarchical adaptive energy efficient cluster routing (HAECR) algorithm. With its hierarchical region division based on node depth in 3D space and cluster head selection based on competition radii and remaining energy, the algorithm is carried out to form clusters of different sizes. In the routing phase, relay nodes are selected from the next layer of cluster heads based on node degree, distance between nodes and remaining energy of nodes. The algorithm effectively mitigates the hotspot problem and further optimizes the uneven transmission load of cluster heads. However, the effect of cluster head coverage redundancy and the influence of a dynamic underwater environment are not considered. The above-proposed clustering algorithms solve the energy consumption problem to some extent, but few algorithms consider the impact of uneven distribution of sensor nodes on clustering.