## 3 Related Works
To that purpose, different regression and neural network methods were proposed in this study as supplemental methods for optimization models for predicting the network parameters and energy consumption of underwater nodes [7]. In the same year, [PERSON] et al. investigated distance-dependent RSSI localization techniques. The placements of the subsea nodes were determined by the authors, and the MEEs can be approximated. A network field of one hundred meters on each side and one hundred meters in total length was required to take distance measurements. The sensor nodes were given the freedom to communicate with one another in both directions. The living nodes in the network, known as the securing nodes, were the only nodes in the network that did not move from their initial position at any point in time. To acquire MEE readings, the sensor node was distributed at random, and the process took place while the readings were being collected. Following the arbitrary positioning of the sensor nodes, a series of experiments were carried out [8]. In October 2023, [PERSON] et al. attempted to monitor underwater sensors, which may carry critical data that must be concealed to avoid eavesdropping. To address these issues, an optimization model is developed to assess the impact of multi-path routing, packet duplication, encryption, and data fragmentation on the lifetime of UASN. However, the proposed optimization model has a very long solution time, and there are occasionally no feasible solutions available. As a complement to optimization models, this paper proposes various regression and neural network methods for predicting the network characteristics and energy consumption of underwater nodes [9]. [PERSON] et al. introduced the MCN-LSTM technique, which leverages deep learning by combining Multiple Convolutional Networks and long short-term memory networks. This combination of techniques detects anomalies in multivariate time series data, allowing for the identification and flagging of unexpected patterns or values that could indicate water quality issues. Water quality data discrepancies can have far-reaching consequences. A method for detecting anomalies in water quality data in real time was presented, using Multiple Convolutional Networks and long short-term memory (MCN-LSTM) technology. The quantitative results demonstrate MCN-LSTM's potential to improve decision-making processes while avoiding negative consequences from undetected irregularities [10]. The same year, [PERSON] and [PERSON] introduced the SIMO-Underwater Visible Light Communication (UVLC) technology. It is a sort of underwater communication technology that transmits data through visible light, specifically the blue-green spectrum. This system used a Single Input Multiple Output (SIMO) design, which means it had one transmitter and several receivers to improve communication reliability and performance. UVLC systems obtained higher data rates than standard acoustic communication systems, making them appropriate for applications that require large bandwidths. In addition, the blue-green spectrum was chosen due to its lesser absorption in water, allowing for more effective transmission. However, the study encountered significant problems, including absorption, scattering, and turbulence in water, which reduced signal quality and range [11]. Following, [PERSON] et al. reported a performance evaluation of vertical Visible Light Communication (VLC) links in mixed water media, which is an exciting field of research, particularly for underwater communication systems. This study typically involved examining several performance indicators such as the bit error rate (BER). As well as an outage probability under varying situations like high turbulence, pointing errors, and transceiver misalignment [12]. In February 2024, [PERSON] et al. investigated intelligent optimization algorithms, and robot collaboration is an intriguing and complicated topic.