## 3 Proposed MNS-CBRP Design Parameters  
In the section that follows, we shall describe the cooperative UWSN model. In Figure 5, the yellow underwater acoustic sensors represent CH nodes, while the white ones represent non-CH nodes, indicating the structure of the clustering. The black line represents  
Figure 4: MNS-CBRP process flowchart. direct communication between two nodes in the CH network, whereas the blue line represents communication between a CH node and the Base Station (BS). Dynamic Coded Collaboration (DCC) is shown by the red dashed line [26; 27; 28]. To solve the issues of energy gaps, drifting underwater nodes caused by ocean currents, and insufficient bandwidth in UWSNs, we proposed a Cluster-Based Routing Protocol backed by Member Nodes (MNS-CBRP). This protocol's formal name is MNS-CBRP. The proposed MNS-CBRP technique extends the K-means method to allow for dynamic CH selection based on node distance from the centroid and residual energy [29]. The MNS-CBRP technique is used to cluster data. The Q-value function considers not only how much energy is expended by the node to transmit a packet to the CH but also how much energy is expended by the CH to send the packet to the BS. DCC is employed during the data transfer portion of the operation [30; 31; 32; 33; 34; 35]. The proposed MNS-CBRP technique will also contain an ocean current model to simulate the influence of node drifting. CHs and cooperative nodes can be selected on the fly in order to enable the network to accommodate mobile nodes. The simulation results suggest that the proposed MNS-CBRP scheme has the ability to minimize total energy consumption while increasing network lifetime when compared to the current scheme [36]. Figure 5 represents a UWSN interaction model used in the MNS-CBRP approach. In this paper, we suggest a distinct routing protocol with the purpose of concurrently improving the protocols' reliability and efficiency. This is accomplished by improving data transfer metrics, attaining optimal energy usage, and extending network lifetime. The network's performance indicators can be improved with the help of MNS-CBRP, the first of the proposed solutions [37]. A three-dimensional network was sliced into identical circles to generate the clusters. Following that, the data packets are gathered by a cluster head node, also known as an anchor node, which is responsible for gathering them from a group of randomly scattered source nodes around the network. The data packets transmitted by CHs are collected using a network comprised of a large number of surface sinks [38]. UWSNs frequently demand a large amount of power to function properly. Lowering the system's power consumption, on the other hand, not only improves the lifespan of  
Figure 5: UWSN interaction model used in the MNS-CBRP approach. the sensor devices but also allows applications to run on battery power [39; 40; 41; 42]. Inventors of battery-powered devices have more leeway for experimenting, allowing them to venture into domains where they might not otherwise succeed. Low-power wireless sensor networks have the potential to be extremely useful in these settings. Wireless sensor networks that utilize \"low power\" contribute to UWSN reliability by lowering the amount of power consumed by individual sensor nodes [43]. In low-power wireless sensor networks, the amount of current that may be pulled from devices while they are not in use is limited [44; 45; 46; 47]. These constraints are intended to assist lower the total amount of electricity required to run the networks. To do this, the power statuses of the network-connected devices are altered to various states. In addition to the energy, we calculated the usual latency during transmission as well as the total amount of time spent in transmit mode [48; 49; 50]. The following is the Algorithm 1 for Cluster head Node selection.