# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Testing Framework
\begin{table}
\begin{tabular}{c l l} \hline \multicolumn{1}{c}{**Sr. No.**} & **Parameter (Symbol)** & **Value** \\ \hline
1 & Operating Frequency (\(f_{0}\)) & 10–20 kHz \\ \hline
2 & Bandwidth (\(B\)) & 10 kHz \\ \hline
3 & Depth (\(h_{0}\)) & 100 m \\ \hline
4 & Transmitter Depth (\(h_{t}\)) & 20 m \\ \hline
5 & Receiver Depth (\(h_{r}\)) & 50 m \\ \hline
6 & Channel Distance (\(d\)) & 1000 m \\ \hline
7 & Spreading Factor (\(k\)) & 1.7 \\ \hline
8 & Speed of Sound in Water (\(c\)) & 1500 m/s \\ \hline
9 & Speed of Sound in Bottom (\(c_{2}\)) & 1200 m/s \\ \hline
10 & Variance of Surface Fluctuations (\(\sigma_{s}^{2}\)) & 1.125 \\ \hline
11 & Variance of Bottom Fluctuations (\(\sigma_{b}^{2}\)) & 0.5625 \\ \hline
12 & Surface Variation Amplitude (\(A_{w}\)) & 0.05 \\ \hline
13 & Surface Variation Frequency (\(f_{w}\)) & 0.01 \\ \hline
14 & Number of Intra-Paths per Path (\(S_{p}\)) & 20 \\ \hline
15 & Mean Intra-Path Amplitude (\(\mu_{p}\)) & 0.025 \\ \hline
16 & Variance of Intra-Path Amplitude (\(v_{p}\)) & \(10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters for underwater acoustic channel modeling. We used a trained ONNX inference model from [20], which consists of two convolutional layers, each followed by bias addition, ReLU activation, and a max-pooling layer. After the convolution and pooling stages, the output is flattened (reshaped) and passed through a fully connected (dense) layer, which outputs a 10-dimensional vector of class scores. The trained CNN inference model receives and performs the classification on the input image data. The output of the CNN model, which contains the classification probabilities, is converted back into a tagged stream using the PDU to Tagged Stream block. The result of the classification is the element with the highest predicted probability which is identified by an index. In the rest of this section, we associate the numbers from 1 to 9 with 9 different classes of fishes, from the most common type (1) to the rarest and most vulnerable type (9). The second component of our framework, by exploiting the result of the classification process, selects the communication parameters such as packet length, gain, and modulation scheme to be utilized in the communication process. An example of the choice of the communication parameters for the nine classes is reported in Table 2 (In order to increase the reliability of the designed system and to cope with possible classification anomalies coming from the detection of new unclassified species, multiple back-up solutions can be designed. More specifically, in case of anomalies in data classification, additional data, for example, about vocalisms of mammals, can be employed so as to assess or correct classification results by means of integrated audio data. Another approach, which can also be used as a complementary mechanism to the above described one, is to transmit, in case of misclassification, the entire raw data while setting the transmission parameters in such a way as to preserve data delivery in any of the cases. The multiplicity of possible situations, which can be incurred in an unknown scenario like the marine one, clearly identifies the importance of figuring out a tradeoff between the preservation of reliability in data monitoring and the energy efficiency in data transmission). Table 1 groups the classes assumed to be associated to species, based on the relevance and rarity of mammals. More specifically,  
* _Classes_ 1-3 (associated to common species). In this case, modulations QPSK or BPSK are considered (with higher occurrence of QPSK modulation exhibiting higher spectral efficiency) with large packet length and high gain to maximize throughput when acoustic disturbance is not a concern. * _Classes_ 4-6 (associated to vulnerable species).