## 1 Introduction
As a result, the team chose acoustic waves as the next best alternative. The acoustic wave, often known as the sound wave, is used for the majority of underwater communication [9]. Acoustic waves can transfer high-quality data and images when paired with acoustic modems. Underwater, acoustic waves can travel up to 100 km. As one gets away from a source, the available bandwidth drops. Even in difficult underwater conditions, acoustic waves may safely carry data over long distances and at low frequencies. However, because of the softer component of the acoustic signal, an acoustic wave is not a comprehensive solution. UWSN disadvantages include slower data transmission speeds, longer communication delays, greater energy costs, and shorter network lifetimes [10]. Figure 2 represents a UWSN interaction model used in the real scenario. The clustering of optical sensors is a critical component of the proposed UWSN concept; however, it is not commonly implemented in current systems. A search of the relevant literature indicated that underwater clustering is nearly exclusively employed for acoustic sensors, while very little is written on optical sensor clustering in the deep sea. This is in contrast to the scenario on land, where much more information is available. However, because of our research into the numerous current clustering strategies for acoustic nodes, we were able to refine our own strategy for grouping optical nodes [11]. Figure 2: UWSN interaction model used in the real scenario. We needed to broaden our understanding of generalized clustering schemes in UWSN; thus, we researched many acoustic sensor clustering techniques presented in the published literature. The first significant finding is that the great majority of UWSN clustering methods are just modified and better versions of Terrestrial Wireless Sensor Networks (TWSN) clustering strategies. Algorithms such as Low-Energy Adaptive Clustering Hierarchy (LEACH) [12], Group Adaptive Filtering (GAF) [13], and Hybrid Energy Efficient Distributed Clustering (HEED) [14] are adopted from their terrestrial counterparts and applied in the underwater environment, but with a few adjustments to account for the unique challenges of the marine setting. To begin, an improved, condensed, and effective clustering technique is required to deal with the significant propagation delays caused by water as well as the non-rechargeable nodes found in underwater networks. Second, the United States Navy Wireless Network will never include the Global Positioning System (GPS). Because high-frequency radio waves cannot go very far into the ocean, any unique suggestions for developing a dependable and energy-efficient clustering technique that takes into account the peculiarities of UWSN would be greatly appreciated. The authors present a clustering technique for an underwater acoustic wireless sensor network that operates on a three-dimensional grid. Following the segmentation of the entire UAWSN network into a number of grids, cluster heads are selected using a sleep/wake cycle. The network that surrounds the cluster head is built by nodes that respond to a message broadcast by the cluster head. The TWSN GAF method [15] has been modified to function in 3D underwater acoustic sensor networks. The initial implementation of the GAF method required only a simple two-dimensional data structure. BGAF is an acronym that stands for \"Based on the GAF algorithm\", which also gives rise to the name. When selecting a cluster head, the quantity of unused energy and the distance between the sound sensors and the surface buoys are taken into account. When neighboring head nodes consume too much energy, the BGAF algorithm immediately activates its built-in transit mechanism. The authors of [16] describe a K-means-based improved version of the TWSN clustering technique that may be employed for underwater acoustic sensor networks. When choosing cluster heads, the density of the nodes in the ocean as well as their depth, are taken into account.