## 2 Literature Review  
[PERSON] and [PERSON] [30] proposed an optimum clustering for UWSN that is compliant with free space optical (FSO), acoustic, and electromagnetic (EM) wave-based transmission systems. In particular, the applicability of the above-mentioned methods for underwater transmission is examined and their efficiency is compared based on optimal clustering and energy utilization [31; 32]. [PERSON] et al. [33] proposed a hybrid clustering model based on moth-flame optimization (MFO) and fuzzy c-mean (FCM) techniques for improving the efficiency of the system. The concept is to create an energy-effective cluster by utilizing FCM and later using an optimization method to select an optimum CH within all the clusters [34; 35]. In [PERSON] et al. [36], a dynamic hierarchical clustering data collection method based on multi-criteria decision making in 3D UWSNs was developed. First, the whole monitoring network is separated into different layers. In order to select a CH in all the layers, the multi-criteria decision-making of hierarchical fuzzy integration and the analytic hierarchy process (AHP) is adapted. Moreover, they utilized a sorting approach for the formation of a clustering topology model to resolve the problems. Next, they proposed an energy-balanced routing method between clusters [37; 38]. [PERSON] et al. [39] proposed a clustering method based on the discrete particle swarm optimization (PSO) algorithm. Next, the clustering method they used was shown to keep UASN clustered, but with the CH moving around [40; 41]. [PERSON] et al. [42] designed an energy optimization clustering algorithm (EOCA) for the multihop underwater cooperative sensor networks (UWA-CSNs) because of the constrained energy source of the underwater sensors. The presented systems consider various factors, like the distance between the underwater sensors and sink nodes, the number of neighboring nodes, the RE of all the nodes, and the movement of the sensors produced by the ocean current [43; 44]. [PERSON] et al. [45] developed an energy utilization balanced protocol, called dynamic clustering k-means (DC-K-means)-based simplified balanced energy adaptive routing (S-BEAR), to extend the lifespan of underwater sensor networks (UWA-SN) by avoiding the energy hole and balancing the energy utilization of underwater sensors. [PERSON] et al. [46] presented a novel approach called the distance- and energy-constrained k-means clustering system (DEKCS) for the selection of CH. The potential CH is chosen according to its residual battery level and location in the cluster. Then, the remaining energy threshold set for CHs is dynamically upgraded to guarantee that the network completely runs out of energy before it is disconnected. [PERSON] et al. [47] proposed a genetic algorithm (GA)-based enhanced mutation operation, an encoding system, and a crossover operation. In addition, the back propagation neural networks (BPNN) used for data fusion are enhanced by adopting a momentum technique that could minimize energy utilization by decreasing the amount of transmitted data and eliminating the redundancy of data [48; 49; 50; 51]. The existing studies and their descriptions are discussed in Table 1.