# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Numerical Results  
Underwater acoustic networks have strict energy constraints, requiring adaptive strategies to optimize resource utilization without reducing functionality. As illustrated in Figure 5, we preliminarily studied the feasibility of the proposed strategy in terms of achievable energy efficiency when compared with methodologies not employing AI-driven design. Three approaches were considered, i.e., AI optimized when no effort in inference is accounted, AI with the consideration of on-node inference load, and a baseline approach. The baseline approach uses static communication settings such as default modulation, packet size, and transmission power, which result in consistently high energy consumption. The AI-optimized approach dynamically adjusts communication parameters based on classification results from inference systems, excluding the energy costs of inference itself, to isolate the impact of parameter adaptation. In contrast, the AI with the consideration of the on-node inference load approach integrates on-node lightweight models to locally classify species, accounting for the incurred computational energy costs while simultaneously performing real-time communication parameter optimization. We took the inference time details from [21] to account for the computational overhead introduced by classification. Although on-node inference introduces overhead, the combined benefits of reduced data transmission and context-aware optimizations yield net energy savings (in the order of about 40% upon increasing the number of iterations), as demonstrated in Figure 5. In this figure, each iteration on the \(x\)-axis corresponds to a complete commu  
Figure 4: GNU Radio flowgraph for the communication system.  
nication cycle consisting of sensing, onboard classification using the CNN, and packet transmission over the acoustic link, i.e., 3000 repeated sense-infer-transmit operations. This underscores the viability of embedding inference-driven adaptability directly into nodes, balancing computational and communication costs for sustainable operation in dynamic underwater ecosystems. In the case of RGB images, the increased computational demand can be addressed using energy-efficient hardware accelerators, such as low-power GPUs or TPUs, in combination with lightweight architectures like MobileNet or EfficientNet. Quantization and pruning further reduce model complexity, while input dimensions can be controlled through simple pre-processing techniques such as cropping or resizing to regions of interest. Achieving high accuracy in marine environments largely depends on the quality and diversity of the training data. In our case, the ability of the model to generalize effectively to complex RGB images is significantly influenced by exposure to appropriate real-world underwater datasets. In contrast, when evaluated on the MNIST dataset, our lightweight 4-layer CNN model achieves an overall test accuracy of 99.1%, with individual class accuracies ranging from 98.6% to 99.7%.  
To further evaluate the system performance, after assessing its suitability in terms of energy, we also considered other key performance metrics including BER, throughput, and delay.