# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 2 Related Works  
[PERSON] et al. use a heuristic approach to solving the surface gateway deployment optimization problem. The performance of UWSNs can be increased by deploying various surface-level gateways (i.e., sink for the UWSNs). In addition, this approach can mitigate the high propagation delay in acoustic communications. The position of gateways plays a crucial role in maximizing the benefit [22]. Furthermore, [PERSON] et al. proposed the enhanced deployment schemes for two-dimensional as well as for three-dimensional architectures of communication in UWSNs. They also gave a mathematical analysis for both scenarios. The proposed scheme helps in achieving the goal of using the least number of sensors for efficient sensing and communication tasks. They discussed the robustness of the sensor network in the particular scenario of node failures. They also gave an approximate count of the number of redundant sensor nodes to be deployed to compensate in case of node failures [23]. Moreover, [PERSON] et al. presented an efficient algorithm for node placement in UWSNs. The aim is to enhance the coverage and reduce the average end-to-end delay. They use the new tracking scheme to forecast the sensor node's positions. The deployment is carried out by using two factors, such as the current location of sensor nodes as well as predicted locations of sensor nodes [24]. [PERSON] et al. have proposed a unique approach for selecting the relay nodes in UWSNs. They use the linear programming approach for the relay node selection with the principle aim of enhancing the network lifetime. They also implemented a routing metric that considers both the transmitting energy and the residual energy [25]. Additionally, [PERSON] et al. presented a scheme for relay node selection in UWSNs based on harvested energy levels. For the selection of the correct signal at the destination, they used a fixed combined ratio. Furthermore, they used an amplified forwarding scheme for data forwarding. In addition, they used the piezoelectric effect-based harvesting scheme to increase the efficiency of sensors in UWSNs [26]. [PERSON] et al. have proposed an efficient routing scheme named vector-based forwarding (VBF). It is a robust, scalable, and energy-efficient geographic routing. This scheme is also called a position-based technique. In this scheme, there is no need for detailed state information. The scheme is called a robust scheme because it is immune to packet loss and node failure. The data transmission mechanism consists of a virtual pipe. In the case of dense deployment, energy consumption is increased. The sparse networks have a low packet delivery ratio (PDR), but, in the case of dense networks, it increases. This scheme has a lower end-to-end delay. The end-to-end delay is minimum in a dense environment. However, this scheme produces communication overheads [27]. Towards enhancing the vector-based forwarding, [PERSON] et al. have given a location-based routing technique known as hop-by-hop vector-based forwarding (HH-VBF). Each forwarder has a separate routing vector in the network. The separate routing vector for each forwarder brings several advantages and achieves a high efficiency for sparse networks. This scheme has a higher packet delivery ratio in sparse networks. This scheme has unique the characteristic of recognizing the routes, particularly when nodes are extremely scattered in the network. In this scheme, if node density increases, then, as a consequence, end-to-end delay, as well as energy consumption, also increases [28]. In addition, UWSNs are blighted by one of the most prominent issues, known as the routing under the void scenario. [PERSON] et al. have proposed a unique approach to solving this problem. This approach is known as vector-based void avoidance (VBVA). This approach has two parts, vector-shift and back-pressure. This scheme does not require prior knowledge of network topology. This technique is a geographic-based routing. This approach enhances the robustness of the network [29]. Next, [PERSON] et al.