## 3 Related Works  
Generally, machine learning (ML) is defined by sensor network designers as a set of tools and techniques used to build prediction models. Experts in machine learning, however, acknowledge that it is a rich topic with a wide range of themes and patterns. In the year 2020, [PERSON] et al. published a survey that used deep learning approaches to determine the untapped resources contained in water. [PERSON] referred to a system's ability to acquire a certain task from data or experimental surveillance beneath the water. The data gathered should be correct, complete, and meet the criteria of the task at hand. Underwater data gathering is difficult, due to sensor mobility caused by sequences of water drifts per second. The authors discovered that a significant amount of packet drop occurs as a result of underwater conditions that impede the data collection procedure. Furthermore, different systems for collecting data below the water's surface already exist, but they are not properly organized [4]. In January 2021, [PERSON] et al. introduced a chapter for readers regarding the concept of sensor networks in underwater environments. The scientists also discussed the potential ways for recognizing underwater activities using a sensor that evaluates real-time information. The authors demonstrated that there are a few problems connected with sensor networks. As a result of their feasibility and adaptability in complicated problem contexts, machine learning (ML) approaches are perfect for successfully resolving such difficulties. Therefore, numerous ML strategies have been described to improve the operational performance of WSNs, particularly underwater WSNs (UWSNs). The goal of this work is to comprehend the ideas of UWSNs and the role of ML in addressing UWSN performance difficulties [5]. In the year 2022, [PERSON] and [PERSON] developed a cluster head election method that circulates the cluster head role among nodes with higher energy levels than the remaining nodes in the network. The researchers used a combination of LEACH and deep learning to extend the network life of the WSN. A convolutional neural network (CNN) was used to select cluster heads in this proposed method. In this study, they combined the benefits of a deep learning method with LEACH to improve network performance. In comparison to the usual LEACH process, the simulation results clearly show that the proposed methodology performs better. Deep learning is used to select the CH, which expands the WSN's network longevity [6]. In May 2023, [PERSON] et al. developed machine learning (ML) models to forecast network characteristics and energy consumption of underwater nodes as supplemental methods to optimization models. To that purpose, the authors used Scikit-learn (1.5.2) and Keras (2.12.0) are powerful tools for building and deploying ML models, for creating several regression models and neural network-based models and then examined their performance using score and error measures. They attempted to run their optimization model with various combinations to acquire data for the models. In addition, they have advocated packet duplication and multi-path routing methods in the literature to meet communication dependability criteria. Furthermore, underwater sensors may transmit sensitive data that must be hidden to prevent eavesdropping. The most generally used method for improving network security is cryptographic encryption. Nonetheless, data encryption necessitates computations to cypher the data, which uses extra energy and reduces the network's lifespan. To address these issues, an optimization model has been suggested to assess the effects of multi-path routing, packet duplication, encryption, and data fragmentation on the lifetime of UASNs. However, the proposed optimization model's solution time is extremely long, and it does not always produce viable solutions.