## 5 Performance Evaluation  
In order to evaluate the performance of the proposed scheme, an event based simulation model for an UWSN with 100 sensor nodes uniformly distributed at a depth of 300 m, is developed in MATLAB. The acoustic channel attenuation is modeled with fixed channel losses \(A_{{}_{0}}\) of 30 dB [14], and channel noise is implemented as described in Section 2. In the simulation model, a target SNR of 20 dB is considered to achieve a packet error rate of 10\({}^{\text{-s}}\) for binary phase shift keying (BPSK) modulation. In addition, the nominal speed of sound is assumed to be 1500 m/s. The transmission parameters of a WHOI Micro-Modem [14, 32] are used for the acoustic channel model. Some of these parameters are listed in Table 1. We assume that the receiver is equipped with decision feedback equalizer and error-correction software that consumes additional energy of 500 mW. Furthermore, we also assume that a 128 MB high density RAM and a 32 GB microSD card are installed on every node to meet the temporary data storage requirements. It is considered that an AUV moves with constant speed of 2 m/s at a depth of 250 m. For the network of 100 sensor nodes, 100 iterations of [PERSON]'s algorithm [29] are run to compute uniformly distributed generator points. It is an iterative algorithm that calculates generator points using the knowledge of sensor nodes' location. It starts with generation of randomly distributed generator points in a plane. In each iteration, a new set of generator points are calculated using Equation (9), after calculating Voronoi regions using Equation (10). The variance of nodes' distances from the corresponding generator points and the number of iterations in the algorithm are used as the stopping criteria for running the algorithm. Regarding traffic generation at each node, the Poisson arrival process is employed. Figure 7 shows, the energy consumed by all sensor nodes during NP_Phase with respect to mean inter-node distance \(d_{m}\). In AUV-CH, based on the assumption that clustering takes place in a similar way as described for AUV-PN, the energy consumption for network partitioning is calculated as  
\[E_{p}=E^{Clustering}+E^{AUV-NPT} \tag{13}\]  
Where \(E^{Clustering}\) and \(E^{AUV-NPT}\) are the energy consumption during the cluster-head setup and the reception of partitioning information from the AUV, respectively. In AUV-PN, the energy consumption for network partitioning is calculated as  
\[E_{p}=E^{Clustering}+E^{AUV-NPT}+E^{sub-clustering} \tag{14}\]  
where \(E^{sub-clustering}\) is the energy consumption associated with the additional process of subclustering and PNs selection after cluster-head setup. The effect of additional traffic generated in AUV-PN becomes more prominent for higher values of \(d_{m}\). Even though the proposed scheme has an additional component of energy consumption \(E^{sub-clustering}\), it will be shown that proposed scheme outperforms other candidates in terms of total energy consumption. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Data Rate & 2500 bps \\ DATA packet length \(L_{data}\) & 1024 bits \\ Control packet length \(L_{cont}\) & 64 bits \\ Initial energy \(E_{i}\) & 1000 J \\ Transmitter efficiency & 0.25 \\ Channel bandwidth & 4 kHz \\ Center frequencies \(f_{control}\), and \(f_{data}\) & 25, 20 kHz \\ Max. transmit power & 50 W \\ Receive and idle-listening power & 300 mW (Micro modem) + 500 mW \\  & (Processing power) \\ Number of clusters & 4 \\ Energy threshold \(E_{th}\) = 0.70 \(\times E_{i}\) & 700 J \\ Vertical link (Relay node\(\rightarrow\)AUV) distance & 50 m \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 7: Energy consumption during NP_Phase, \(E_{p}\).