## 2 Literature Review  
This section provides a brief overview of UWSN's existing cluster-based routing techniques. [PERSON] et al. [11] developed a low-energy adaptive clustering hierarchy (LEACH) approach for balancing this node's power utilization and enhancing the network's lifespan. In regards to depth levels, the network regions are divided into layers. The data collected by the nodes are routed via multi-hop routing paths to an SN. The CHs are selected based on the node's depth. The CH collects the information packets of each cluster member and then forwards them to the SN's upper layer in order to send information from the nodes to the SN. [PERSON] et al. [12] presented a novel system for UWSN based on cooperative energy harvesting. The system employs the AF technique at the relay node to convey data and the FCR method at the end node to choose accurate signals. The presented technique chooses relay nodes from among its neighbor nodes based on the amount of energy gathered. Almost all of UWSN routing techniques based on cooperation do not include energy harvesting mechanisms at the relay nodes. EH-ARCUN incorporates piezoelectric energy harvesting at the relay node to increase the work capacity of sensor nodes in the UWSN with an energy-based cluster optimization algorithm. [PERSON] et al. [13] design an energy-efficient UWSN capable of reducing energy costs and increasing efficiency in underwater settings. A UWSN framework is built in the provided cluster-based underwater wireless sensor network (CUWSNs), which takes use of the benefits of CH and multi-hop transmissions. The described CUWSN enhances a network lifespan by multi-hop broadcasts. [PERSON] and [PERSON] [14] offer the EERBLC method, a localization-free routing mechanism. EERBLC protocols are divided into three stages: cluster update and maintenance, transmission routing, and layer and uneven cluster construction. Initially, the monitoring regions under the sea were divided into levels, and nodes in comparable layers were grouped. A novel un-equal clustering strategy based on layer for UWSN is described in order to balance energy across whole networks and prevent \"hotspot\" concerns. [PERSON] and [PERSON] [15] provided a method for fuzzy-based CH selection that increases network longevity. Then, they created a fuzzy-based routing technique that significantly increases the packet transmission rate. In comparison to the current routing protocol, they have adequate power consumption and, hence, prolong the overall performance of the network for overcoming the problems underwater. To protect against internal attacks, [PERSON] et al. [16] describe an LTMS technique based on binomial distribution. Simultaneously, energy, distance, environment, and security domains are considered and presented to propose an MSCR system through dynamic dimension weight in hierarchical WSN. [PERSON] et al. [17] present a unique routing strategy known as the EE-MDCHSRP technique, which is suggested to the UWSN for successful dynamic CH choices. The dynamic CH node is defined by taking into consideration the residual energy factor, the least mobility factor, and the node density. [PERSON] et al. [18] describe three energy-efficient routing strategies for monitoring the field with circular and square geometries for underwater WSNs; they are sparsity-aware energy-efficient clustering based on circular depth, sparsity-aware energy-effective clustering routing protocol, and circular sparsity-aware energy-efficient clustering. Each protocol is designed to reduce the power needs of sparse areas, while density search techniques are used to locate dense regions and sparsity search algorithms are presented to find sparse network fields. [PERSON] et al. [19] suggested two network frameworks with numerous sinks: the VH-ANCRP technique for addressing local maximum nodes and the ANCRP approach for obtaining consistent data transmission metrics. To form clusters, the network areas are divided into smaller cubes. As a CH, all cubes are assigned using anchor nodes [20].