# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
## 5 Performance Evaluation of Underwater Network  
We carried out a set of simulations devised to assess the different performance of the optical and acoustical communication networks under different system and topology conditions. In particular, we assumed that the optical communication link can take place if nodes are within a given range, independently from the mutual position (angle) of the transmitter/receiver. Acoustical communication can be active as well but on a larger range and with a lower data rate. Simulations were performed using a custom numerical simulator, tuned with the parameters of the Venus device reported in Section 4. The considered network scenarios comprise a variable number of nodes that are spaced to cover a planar area. We evaluated performances of two network scenarios: a linear and a grid grid topology. In the linear topology, nodes were arranged on a line over 100 m while, in the grid topology, nodes were displaced in a regular grid to cover a 50 m \(\times\) 50 m area. The average distance among nodes varied according to the turbidity of the water and was set to the maximum communication distance (DV) decreased by a margin of 20%. Furthermore, a positioning error was considered to account for the possible estimation error of the AUV with respect to other nodes. The error was modeled as a bi-dimensional Gaussian random variable with \(\mu=0\), \(\sigma=0.1\cdot DV\). Figure 6 shows an example of a generation of the grid topology for various values of the parameter \(K\). Figure 5: Maximum achievable distance for a given SNR for different diffuse attenuation coefficient K. \begin{table}
\begin{tabular}{l l l} \hline
**NEP Contribution** & **Description** & **Value** \\ \hline \(P_{bg-sn}\) & Noise power accountable to the shot-noise for the solar irradiation & \(1.23\times 10^{-12}\) W \\ \(P_{dg-sn}\) & Noise power accountable to the shot-noise of the received signal & \(0.22\) W \\ \(P_{dark-sn}\) & Noise power accountable to the shot-noise of the dark current of the photo-diode & \(1.25\times 10^{-11}\) W \\ \(P_{amp-n}\) & Noise power accountable to the current of the pre-amplifier & \(1.97\times 10^{-11}\) W \\ \hline \end{tabular}
\end{table} 
Table 3: Main NEP contributions. **Networks delays**: Figure 7 shows how the quality of water affects the delay performance of the network considering different topologies. Specifically, it reports the Cumulative Distribution Function (CDF) of the network delays under the following topological conditions. There is a variable number of nodes that must cover 100 m adopting a quasi-linear topology. The nodes are spaced on average by \(D/2\) where \(D\) is the maximum range of optical transmission that depends on the turbidity of the water. Node position is affected by a Gaussian error with \(\sigma=10\) m. We selected a pair of nodes uniformly from the available ones and evaluated the delay of the multi-hop network communication. In Figure 7, variation of the turbidity of the water corresponds to a greater communication delay as a consequence of a greater number of nodes involved in relaying the communication from the source to the destination nodes. In the case of clear water (small K), we have a delay of about 0.01 s while, in the case of high turbidity, the delay grows up to five times. Figure 6: Example of node grid networks for different diffuse attenuation coefficient K. (Axes are in meters). Figure 7: Delay distribution probability of randomly selected pair of nodes, in the case of a linear topology. **Energy for point to point transmissions**: To evaluate the energy required to provide the multi-hop data transmission, we selected the same network scenario in which every node has to transmit a given amount of data (offered traffic). In Figure 8, there is almost one order of magnitude of difference in terms of required power for the different values of \(K\). This is a fundamental parameter since the AUVs are battery powered and hence energy constrained.