## 2 System Model with Collision Design and Analysis of Our Proposed System
### Avoiding Collisions Between Clusters
#### 2.2.4 System Model  
Figure 5 illustrates our chosen network system that uses a cluster topology for the collection of communicating data packets.  
Figure 4: Tackling the hidden and exposed node challenges in UWCNs.  
In this network, there are four clusters and each cluster has a central CH. Furthermore, the CH is uniformly encircled by a large number of Rx nodes within a single hop. The Rx nodes collect all the communicated data packets of the area and directly forward them to the CHs. Similarly, the sink node uses only a one-hop technique for collecting communicating data packets from the CHs, and then, transmitting them to the sensor Rx node. All Rx nodes are assumed to have the same battery lifetime, processing power, and transmission range. The hidden node issue results from Rx nodes being placed randomly across the overall network. Both inter-cluster and intra-cluster interferences are included in the analysis of this research. Additionally, any motion caused by features, including ocean waves and current movement, has not been considered since the nodes are regarded as static. For channel access, we use a traditional mechanism based on the handshaking MAC technique that follows the RTS/CTS and communicating data packet acknowledgment cycle. By controlling the magnitude of IR, the LIRC issue may be successfully solved in a more logical manner. The aforementioned objective may be accomplished by allocating the ideal transmission energy for transmitting the data packet. The transmission energy must satisfy the following requirements:  
\[SINR_{D,P}\geqslant SINR_{dTH}, \tag{4}\]  
where \(SINR_{dTH}\) is the minimal \(SINR\) threshold required to correctly decode a received communicating data packet, and \(SINR_{D,P}\) stands for the signal-to-interference and noise ratio of the communicating data packet. Equation (1) may also be written as follows:  
\[SINR_{D,P}=\frac{RX_{P}}{N+I_{S}}\geqslant SINR_{dTH}, \tag{5}\]  
where \(N\) denotes the ambient noise, \(I_{S}\) denotes the interference signal, and \(RX_{P}\) denotes the energy level of the communication data packets arriving at the Rx node. If \(S_{Tx}\) stands for sender and \(R_{Rx}\) stands for receiver, assume that the energy of a communicating data packet is \(E_{D,P}\) of \(S_{Tx}\). Therefore, according to [48; 49], we can describe the \(RX_{P}\) as:  
\[RX_{P}=\frac{E_{D,P}}{A_{draft}}, \tag{6}\]  
Figure 5: Multi-cluster underwater wireless sensor network.  
where \(A_{dsr,f}\) stands for the attenuation function, which is considered for acoustical path loss for a central frequency \(f\) and distance \(A_{dsr}\) between \(S_{Tx}\) and \(R_{Rx}\). Given that the interference occurs outside of the RTS/CTS communication range, the following equation may be used to express the interference signal's maximum value:  
\[I_{S}=\frac{E_{\max}}{A_{dIR,f}}, \tag{7}\]  
where \(E_{\max}\) denotes the permitted maximum energy for transmission, and \(A_{dIR,f}\) is the attenuation across \(I_{S}\) and \(R\) when they have been divided by the distance \(dIR\). The noise \(N\) may be roughly represented as follows (as stated in [48,49]):  
\[N=50-18\log(f). \tag{8}\]  
Furthermore, by including (2)-(5), we may modify (2) as:  
\[I_{S}=\frac{\frac{E_{D,P}}{A_{dsr,f}}}{50-18\log(f)+\frac{E_{\max}}{A_{dIR,f}} }\geq SINR_{dTH}. \tag{9}\]  
As stated in (6), the following requirements must be satisfied to decode the signal that was received if there are many interferences:  
\[\frac{\frac{E_{D,P}}{A_{dsr,f}}}{50-18\log(f)+\frac{E_{\max}}{A_{dIR,f}}}\geq SINR _{dTH}. \tag{10}\]