# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 1 Introduction  
Underwater acoustic sensor networks (UASNs) are available to revolutionize our ability to monitor marine ecosystems, conduct oceanographic studies, explore underwater resources, and enhance maritime safety. However, deploying effective underwater sensor networks, especially when using widespread acoustic technologies (In this work, we focus on acoustic underwater communication networks due to their widespread adoption in commercial scenarios), poses challenges due to propagation in the complex underwater medium exhibiting limited bandwidth, high delay, and signal attenuation. These limitations, combined with significant energy constraints at the equipment level, the impracticality of battery replacement, and finite operational lifespans, necessitate the design of energy-efficient strategies such as adaptive communication protocols, routing, scheduling, and cross-layer optimization to ensure network longevity.  
A typical exemplary application for these technologies is non-invasive marine life monitoring, which requires novel approaches to detect and track underwater creatures while minimizing environmental impact [1] and energy usage, while also maintaining network functionality. Intelligent software-defined radio (SDR) systems powered by artificial intelligence (AI) can adapt transmission parameters based on the type of marine species monitored and identified in the surrounding of the sensor nodes, optimizing communication parameters for sustainable underwater sensing. Traditional sensing network architectures, which rely on the continuous transmission of raw sensor and image data to centralized surface stations for processing, amplify these limitations, leading to high energy consumption and reduced network longevity. Therefore, fundamentally new approaches to underwater sensing and communication are required to overcome these challenges and enable sustainable and scalable underwater monitoring.  
In response to these challenges, AI emerges as an effective approach, decentralizing intelligence from centralized infrastructures directly to underwater sensor nodes. Employing lightweight AI inference models allows sensor nodes to locally classify marine species; instead of transmitting large files, these nodes share only classification outcomes, drastically reducing data volume. Through adaptive communication parameter selection, the system conserves energy and bandwidth while empowering real-time decision-making in dynamic underwater ecosystems. For instance, when rare species are detected, low-power modes with robust modulation (e.g., binary phase shift keying (BPSK)) and smaller packet length are employed to minimize interference. Conversely, for common species, higher-throughput modulation schemes (e.g., quadrature phase shift keying (QPSK)) with larger packet length and higher transmission power are selected to maximize bandwidth efficiency. This fusion of on-node inference and software-defined networking methodologies minimizes resource usage, energy consumption, and environmental impact.  
To develop this system, a model was designed and simulated using GNU Radio to perform classification and experiment with different adaptive transmission parameters. The system performance was evaluated using key performance metrics such as energy consumption, bit error rate (BER), throughput, and delay. Tests validated the system ability to adapt its transmission strategy based on data semantics, demonstrating how AI-powered SDR systems can balance network performance, operational efficiency, and sustainability.  
The rest of this paper is organized as follows. In Section 2, some related work is discussed. In Section 3, the considered system is illustrated. In Section 4, the channel model being adopted for characterizing the underwater medium is illustrated, while in Section 5, the performance results are reported. Finally, in Section 6, the conclusions are drawn.