## 1 Introduction  
Recent research has witnessed tremendous research interest in UWSNs because of the wide range of applications. Example applications include oil and gas pipeline monitoring, coastline surveillance, underwater mine detection and oceanographic data collection [1; 2]. These networks employ stringent resource-constrained sensors [3] to monitor a phenomena (e.g., the pH level of water) and report it to the sink, which relays it to the surface station [4; 5]. Due to the rapid attenuation and high absorption of radio signals in UWSNs, communication via these signals is not a feasible method. Thus, acoustic signals are widely used for such purposes, as these have a relatively low absorption rate. However, due to the featured low bandwidth and high end-to-end delay of acoustic signals, the routing task in UWSNs becomes highly challenging. Another unique characteristic of the aqueous environment is its highly dynamic network topology, which demands frequent information exchange (significant overhead) between the nodes if the network is to operate properly. Limited energy resources further make the life of nodes in demand for longevity. In short, enhanced energy efficiency, network lifetime prolongation, reliability improvement, end-to-end delay minimization and efficient data gathering are always desired while designing a UWSN routing protocol [6; 7; 8]. For efficient and reliable data collection, most of the existing routing protocols employ a static or mobile sink to the improve network lifetime [5; 9; 10]. The former badly suffers from a hot-spot problem, in which nearby sensors to the sink die out more quickly due to consistent energy consumption in relaying data of distant nodes. To cope with this problem, some researchers employed mobile nodes. The rationale is that continuously changing the neighbours of a sink will result in balanced energy consumption. To minimize energy consumption, a few schemes are proposed that deploy an mobile sink (MS), which moves closer to sensors for data collection. If the network area is increased, then four possible cases are worth discussing. 1. Neither the number of nodes is increased, nor is the AUV added: In this case, the nodes communicate at relatively farther distances. Thus, the performance of the network degrades in terms of network lifetime, throughput, end-to-end delay, _etc._* The number of nodes is increased, but the AUV is not added: Initially, throughput and network lifetime would increase; however, later on, both will decrease due to interference. The end-to-end delay, in this case, would increase. * The AUV is added, but the number of nodes is not increased: The network lifetime and throughput would increase, and the end-to-end delay would decrease. * The number of nodes is increased, and the AUV is added: This case would show hybrid results of Case (ii) and Case (iii). In this research work, we propose an efficient data-gathering scheme using an autonomous underwater vehicle (AUV). We begin our findings by developing a sub-optimal elliptical AUV trajectory via a connected dominated set (CDS). The semi-major and semi-minor axis of the elliptical path are sub-optimized to enhance data gathering in the harsh underwater environment. In this regard, the Monte Carlo simulation method is used to calculate the sub-optimal value of \(\beta\) (defined in Section 4). Next, we precede our findings with the selection of gateway nodes (GNs), which are later on rotated on the basis of residual energy (this rotation balances the network's energy consumption). Soon after the selection of GNs, member nodes (MNs) are associated with GNs by using the shortest path tree (SPT) algorithm [11]. It is worth mentioning here that the MNs are minimized in number, as a suitable number of MNs assigned with their respective GNs leads not only to low energy consumption, but also to a decreased number of dropped packets. In this way, data are gathered with minimum loss and reduced energy consumption. The rest of the paper is organized as follows.