# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Testing Framework  
The overall framework considered in this paper has been implemented in GNU Radio 3.9 [16], while also exploiting external Mathworks Matlab [17] modules for modeling the underwater channel according to the Stojanovic underwater channel model [15] and as illustrated in the previous section. This module indeed accounts for multipath, frequency-dependent loss and Doppler shift, which typically characterize underwater channels. The simulation parameters used in the channel model are summarized in Table 1.  
The GNU Radio implementation of our system includes a classification module, as shown in Figure 2, which leverages convolutional neural networks (CNNs) for classification. CNNs, a type of deep learning model, are designed to process grid-like data such as images, videos, and time-series. They excel at identifying patterns and capturing spatial hierarchies using convolutional layers with filters. Widely applied in tasks like image recognition, natural language processing, and video analysis, CNNs are trained through supervised learning on labeled datasets. Their key advantages include position-invariant feature detection, fewer trainable parameters compared to fully connected networks, and the ability to learn complex data representations. In Figure 2, the pre-processing of each image and conversion to an inference-acceptable format are highlighted. Then, this is fed to an ONNX-Runtime CNN block, and the post-processing of the result as a class label is executed to be later reused for the adaptation logic that triggers the transmission operations. For simplicity, the standard MNIST dataset [18] has been used instead of a dedicated dataset of fish images. This, however, does not impact the applicability of the approach. In case of a different input, e.g., an RGB dataset, the larger images would increase inference latency, yet the transmission time and energy would remain unchanged because the system still sends only the compact class label. MNIST serves as a publicly available, well-controlled benchmark for validating the complete _sense-infer-transmit_ pipeline. The same CNN architecture can be re-trained on RGB marine images using transfer learning [19], without requiring any modifications to the communication chain (Although classification energy increases with image size, a lightweight CNN model running on a Raspberry Pi 4, which typically operates at an active power between 2 W and 4 W, consumes approximately 1 mJ per inference for a \(28\times 28\) grayscale frame. When a lightweight CNN model, such as MobileNet or a custom architecture, is optimized through quantization or model compression techniques like pruning, the energy cost typically remains below 10 mJ for a \(224\times 224\) RGB frame. In contrast, transmitting the same \(224\times 224\times 24\)-bit image which is approximately 150 KB over an EvoLogics 18/34 underwater modem at around 14 kbps across a 1 km link would require keeping the power amplifier active for roughly 90 seconds, resulting in energy consumption in the order of several hundred Joules. This is several orders of magnitude higher than the cost of local inference).