## 2 Literature Review
### Clustering and Data Forwarding  
A data forwarding procedure using a Q-learning model was proposed [28]. Initially, a beacon message was broadcast using a sink through which the sensor identifies its neighbors. The neighbor node was updated with RSSI and angle of arrival. If the sink node was not present within the sensor's one-hop distance, then a forwarder was selected using the Q-learning process according to the transmission probability. The increase in the number of forwarders drained energy in the source sensor as well as in the forwarder. In [29], a UWSN was modeled as disjoint concentric coronas in six partitions. Here, a source sensor node selected intermediate sensors from every wedge and then, using the path, their sensed data reached the sink node. However, this work balanced energy consumption with the formation of a corona for a large-scale environment, which was tedious.  
A depth-based routing mechanism named the energy-balanced, efficient and reliable routing (EBER2) protocol was developed in [30]. A forwarder was selected using three constraints: weighted depth difference, potential forwarder node (PFN) and residual energy. The data transmitting nodes determined distance and selected the nearest sink to deliver the data. The sensor nodes nearer to the sink drained more energy due to the transmission of the sensed data from the nodes in the network. A self-organizing and scalable routing protocol (SOSRP) was operated with the computation of hop count and distance [31]. In this protocol, the neighbors were discovered by exchanging messages and then hop count; distance was estimated for path prediction. The path with the shortest hop count was used for data transmission. In this work, the selected nodes performed poorly in relation to energy consumption, and, hence, the stability of the path was not hardy.  
An energy-efficient multipath routing (E\({}^{2}\)MR) was developed in this paper, which selected a forwarding node from the constructed priority table [32]. By exchanging control packets, the priority table was constructed with the entities of residual energy, distance and priority value. The priority value was computed based on the energy and the depth of the sensor. Here, the node with higher priority value was selected as a forwarding node. Routing was performed effectively with the construction of a cluster that eventually minimized energy consumption. A multi-layer, cluster-based, energy-efficient (MLCEE) protocol was proposed, in which the CH was selected by computing the Bayesian probability and residual energy of the particular node [33]. After the construction of the clusters, the members were allotted with time division multiple access (TDMA) time slots [34]. The CH selected a forwarding node by determining residual energy, hop and probability as a fitness value.  
A QoS-aware, evolutionary, cluster-based routing protocol (QERP) was presented in a 3D environment [35]. The operators involved in the genetic algorithm (GA) were crossover and mutation operators. The key constraints that were taken into account were identity, energy and RSSI. This GA was time consuming, and, also, it was tedious when multiple sensor nodes participated. An adaptive node clustering technique was developed for a smart ocean UWSN known as SOSNET [36]. The moth-flame optimization (MFO)algorithm was applied for optimal performance of routing. The CH was selected by estimating transmission range, residual energy, node density and load balance factor. The fitness value for the optimization algorithm was computed using residual energy, distance with the neighbors, load balancing factor and weight value. The MFO algorithm was slow in convergence, which failed in routing. Therefore, the routing of sensed data packets was required to be faster and effective with the reduction of delay and energy consumption of sensors. Delay in the selection of route also increased the delay in data transmission.