# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 2 State of the Art  
In this section, we will describe relevant papers where the use of artificial intelligence techniques for the purpose of improving communication efficacy in underwater communication networks is proposed. The challenges posed by the realization of an Internet of Underwater Things (IoUT) are numerous and mostly related to constrained energy consumption, the need for high bandwidth, and the unreliability of the medium. These factors are exacerbated by the limited energy resources, the difficulty of battery replacement, and the significant attenuation and scattering of signals in marine environments. In [1], all the abovementioned criticalities have been discussed in the perspective of understanding the potential exploitation of AI technology to transform the conservation, monitoring, and management of marine environments through automation. However, no specific focus on the optimization of communication mechanisms is presented, since the work disregards aspects associated with the design of on-node modulation, power control, or any other adaptive physical-level mechanisms, while presenting a more visionary discussion on potentials. Indeed, the exploitation of AI-based tools can represent a successful way to cope with complexities of marine environments. In [2], the authors proposed a self-learning semantic framework for underwater communication that emphasizes transmitting semantically relevant information only. This approach reduces data redundancy and enhances communication efficiency by prioritizing information freshness and contextual importance; however, it does not incorporate any physical or link layer communication adaptivity, which makes the semantic methodology only aimed at data redundancy reduction. Other studies have similarly leveraged network knowledge before or during the communication process. For instance, in [3], the authors utilized prior knowledge of underwater conditions to adjust network parameters and employ image compression to facilitate image transmission within bandwidth-constrained underwater environments. In [4], a reinforcement learning approach was employed to improve image quality by observing both the received image quality and considering communication parameters at the sink node. This implies an appropriate choice of modulation and coding. Another semantic-based strategy is presented in [5], where images are transmitted in the form of textual descriptions and subsequently reconstructed at the receiver using text-to-image AI models. The primary objective of this method is to reduce transmission delay and enable real-time image communication in critical underwater conditions. In [6], the authors introduced a semantic approach targeting underwater monitoring applications. By employing deep learning-based encoder-decoder architectures within a semantic communication framework, they mitigated underwater channel impairments upon transmitting to offshore stations. For underwater optical communications, the authors in [7] developed a semantic communication framework capable of dynamically adapting transmission data rates in response to changing channel conditions. From the point of view of distributed AI solutions, in [8], a distributed deep learning approach is used on the cloud infrastructure to train a fish segmentation model, thereby enhancing accuracy and efficiency for large marine datasets. After training, they deployed a compressed model on an edge device to enable real-time, energy-efficient inference in underwater environments, minimizing both delay and power consumption. In another study, the authors in [9] integrated distributed intelligence within a hybrid fish swarm optimization algorithm for underwater wireless sensor networks, allowing sensor nodes to autonomously optimize 3D coverage through local decision-making and information sharing. For on-node inference, the authors in [10] presented a battery-free machine learning pipeline for microcontroller units (MCUs), optimized using proxyless neural architecture search (ProxylessNAS) and static quantization.