# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 6 Results and Discussion
### Impact of the AUV Velocity on Performance  
To explore the effect of AUV velocity on the average AoI, we simulated the average AoI performance of the collected data with data arrival rate \(\lambda\) = 20 and 300 Kbits. The experimental results are shown in Figure 4, where it can be observed that the average AoI of the data collected by AUV gradually decreases with increasing AUV velocity. The weighted average AoI of the single UAC is lower than that of the multi-modal and single UOC when the AUV speed is 0.5 m/s. With the increase of the AUV velocity, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Value (Unit)** \\ \hline \(f\) & Carrier frequency & 35 (kHz) \\ \(\Delta f\) & Bandwidth & 2 (kHz) \\ \(k\) & Propagation loss & 1.5 \\ \(SNR_{\min}^{ac}\) & UAC minimum SNR & 3 (dB) \\ \(R_{ac}\) & UAC data rate & 3.16 (kbps) \\ \(R_{op}\) & UOC data rate & 0.5 (Gbps) \\ \(\theta\) & Half of the transmitter & 3 (\({}^{\circ}\)) \\ \(c\) & Extinction coefficient & 0.18 (m\({}^{-1}\)) \\ \(\zeta\) & Turbidity of water quality & 0.05 \\ \(D_{r}\) & Aperture diameter & 0.25 (m) \\ \(SNR_{\min}^{op}\) & UOC minimum SNR & 3 (dB) \\ \(NEP\) & Noise equivalent power & 1 (mW) \\ \(p_{rec}^{op}\) & Average transmitted power & 0.01 (mW) \\ \(\rho\) & Density of water & 997 (Kg/m\({}^{3}\)) \\ \(\eta_{p}\) & Efficiency of the AUV & 100\% \\  & propulsion system & \\ \(C_{D}\) & Drag coefficient & 0.0064 \\ \(A_{s}\) & Wetted surface area & 0.8856 (m\({}^{2}\)) \\ \(B\) & Experience replay buffer sizer & 500,000 \\ \(\Phi_{b}\) & Mini-batch size & 256 \\ \(\gamma\) & Reward discount factor & 0.95 \\ \(\chi\) & Update step & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. the average Ao of the multi-modal data collection scheme is better than that of the single communication option. The primary reason for this performance is that the AUV travels slower and increases travel time, whereas the long-distance data collection via the single UAC is able to reduce the travel time of the AUV, which mitigates the increase in AoI. As the AUV velocity increases, the effect of AUV travel time on AoI is weakened, which makes the weight of data transmission time increase for AoI; thus, the UOC scheme outperforms the UAC scheme at higher AUV velocities. The multi-modal data collection scheme selects the best communication option according to the data characteristics, AUV navigation time and data transmission time so that the overall performance is better than the single-modal scheme. Figure 4: Effect of AUV velocity on AoI of collected data. Figure 3: Convergence performance of the DON-based data collection algorithm. Furthermore, the average AoI of our proposed multi-modal data collection scheme at the AUV velocity of 0.5 m/s is inferior to that of the single UAC scheme; this is because the multi-modal scheme not only considers AoI but also focuses on data collection energy consumption, so it sacrifices some AoI performance to reduce CHs energy consumption. Under the parametric conditions of Figure 4, we analyzed the effect of AUV velocity on the energy consumption of data collection. Considering that the energy consumption of CHs is irreversible, we pay more attention to the energy consumption of data transmission, and therefore, we set \(\varpi=0.03\). As shown in Figure 5, it can be observed that the weighted energy consumption of the single UAC scheme always remains at the highest level owing to the high weight of data transmission energy consumption. The single UOC scheme performs well in terms of CHs data transmission energy consumption, and hence, the weighted energy consumption is better than the single UAC scheme. The multi-modal scheme is able to reduce both AUV energy consumption and CHs data transmission energy consumption by jointly deciding on the best communication option based on packet size and path length.