# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 1 Introduction  
The widely growing application domains for underwater sensor networks (UWSNs) have attracted potential attention in R&D from the Internet of Things (IoT)-oriented industries and academia [1; 2; 3]. The growing domains include border security-centric military applications [4; 5], energy and cost-centricwater-based transport applications [6; 7], oil, and natural gas production applications [8; 9], and developing fishing-centric industries [10; 11]. In underwater networking, tiny sensor nodes are deployed underwater, as well as on the upper surface layer for monitoring the specific underwater area [12]. These underwater nodes communicate with the surface nodes, acting as access points or cluster heads for reaching the sink node of the network, which accumulates the information and communicates with the cloud-enabled computing resources [13]. Underwater networking is significantly challenging compared to traditional wireless networking due to the dynamic self-mobility of the medium of communication and constraints in signal propagation in the underwater environment [14; 15; 16]. In this constrained networking environment, the underwater network deployment-oriented challenges further complicate scientific investigations towards the development of an energy-centric green underwater network for various application domains [17; 18; 19]. Towards enabling green underwater networking, several service and geolocation-centric techniques of varying quality have been suggested [20; 21]. A heuristic approach has been suggested in underwater networking for solving the surface gateway deployment optimization problem, focusing on the quality of service [22]. In particular, a heuristic solution has been explored for the optimal deployment of surface nodes as access gateways for underwater networking. However, the surface gateway deployment optimization lacks coordination with underwater node level multi-hop communication. To support service-centric underwater networking, an optimal underwater node deployment architecture is explored for a 3D underwater network environment [23]. A scientific, mathematical model is designed for assessing the optimality of the deployment architecture. However, the deployment architecture did not integrate with the dynamic self-mobility of underwater nodes in the network environment. The 3D deployment architecture has been further improved by considering static, self-adjusting, and mobility-supported deployments [24]. However, the coordination between static and dynamic nodes during communication is lacking in these three types of deployment architectures. To support these deployment approaches, a linear programming-centric approach has been suggested for selecting underwater relay nodes focusing on a longer network lifetime [25]. In a similar work, node-level energy harvesting capability has been used as a parameter in relay node selection for a longer network lifetime [26]. However, the impact of self-mobility of underwater nodes due to underwater flow is not considered in both approaches, including deployment-centric network lifetime optimization and harvested energy level-centric network lifetime optimization. To improve network lifetime optimization, a geolocation vector-based forwarding strategy has been explored [27]. It has focused on location-centric relay node selection to reduce energy consumption in underwater communication. The geolocation-centric underwater relay node selection has been improved by utilizing hop-by-hop forwarding prioritization for sparse underwater networking [28]. The pure geolocation-centric relay node selection faces the void area issue in underwater networking. To address the issue in geolocation-centric relay node selection, void avoidance approach has been investigated, utilizing the quality of service-oriented underwater backtracking [29].