# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 6 Challenges and Future Trends in Cooperative UWSNs
### The Role of Machine Learning in Future Cooperative UWSNs Management  
The state of the art about mechanisms and protocols for cooperative UWSNs clearly highlights how the proposed solutions are aimed to address the challenges rising from underwater signal propagation, independently of the protocol stack layer they are related to. Such particular condition where the physical issues have a straight impact on network management characterizes the underwater acoustic scenario, while it is less evident in other terrestrial systems. Here, performance depends not only on how data transmission is handled, but also on the amount of signaling employed for network management. In fact, due to the slow speed of sound and long propagation delay, the use of heavy overhead signaling leads to significant throughput reduction. Furthermore, network optimization is typically driven by CSI that must be reliable and updated in order to achieve good results. Anyway, the sudden variation of the channel conditions and nodes mobility require frequent update of CSI, that means large overhead signaling and higher energy consumption for nodes. In this regard, machine learning may potentially represent a revolution for UWAC by enhancing the efficiency, reliability, and robustness of data transmission in challenging aquatic environments. In fact, machine learning algorithms address the mentioned UWSNs challenges by learning from the environment and adapting the communication to its dynamic nature. Regarding the physical layer issues, one significant application regards adaptive modulation and coding, where machine learning models predict the optimal settings to maximize the data rate and minimize errors. Additionally, machine learning enhances signal processing techniques, improving the detection and classification of acoustic signals corrupted by background noise. Moreover, machine learning facilitates the development of access techniques for reducing the amount of interference. Furthermore, it can lead to develop intelligent routing protocols in underwater acoustic networks, ensuring efficient data dissemination and reducing latency. By analyzing patterns and predicting network conditions, these protocols can dynamically adjust routes to maintain robust communication links. Most of the studies in the literature focus on networking aspects such as routing, even though the physical layer components are taken into account as well. For instance, clustering and routing through improved \(k\)-means and \(Q\)-learning algorithms are discussed in [62] to achieve a fair energy consumption among nodes and optimize the bandwidth utilization. Propagation delay, minimal residual energy, and collision rate are the metrics considered in [63] to drive reinforcement learning and \(Q\)-networks algorithm, with the goal to realize an optimal cooperating node selection aimed to improve the network performance in terms of end-to-end delay, reliability and energy efficiency. Avoiding the use of CSI for path selection is considered in many works where contextual bandit approach is applied [64; 65]. Here, relay selection is performed by leveraging environmental data in the learning process directly gathered from sensor nodes, including temperature, wind speed and location. By doing so, CSI acquisition and /or estimation is no more required. Together with convenient path selection, UWSNs performance can be improved by adapting the nodes power based on the estimated environmental conditions. Such strategy is investigated for instance in [66], where the authors exploit a distributed multi-agent reinforcement learning mechanism that, through power optimization, allows the achievement of a balanced trade-off between single link reliability and network performance. Finally, joint power allocation and relay selection are discussed in [67; 68], so that energy efficiency and network capacity issues are simultaneously addressed. In general, further studies are required to investigate the applicability of machine learning-based techniques in the context of cooperative UWSNs.