); [EMAIL_ADDRESS] (A.S.)  
[PERSON]  
1 Department of Computer Science, COMSATS University Islamabad,  
Sahiwal Campus,
delays, high data error rates, long end-to-end delays, ineffective data collection, short network lifespan, continuous topology changes, and higher power consumption are all issues in UWSNs [5; 6]. In an acoustic network, communication distance is minimal [7; 8]. In past years, many routing techniques have been proposed to collect data using multihop routing [9; 10; 11; 12; 13]. However, multihop has many disadvantages such as more energy utilization by sensors, the long delay between sender node to sink node, and high PDR [14]. UWSN architecture consists of mobile sensor nodes that are deployed randomly or in grid topology. These sensor nodes are further divided into two groups: data gathering and sink nodes. Data gathering nodes are deployed underwater at different depths, include acoustic modems for communication, and are mobile. Sink nodes are placed at the water surface, include both radio and acoustic modems used for communication, and are static. In UWSNs, sensor nodes sense the data and send it toward the sink node by using multiple intermediate nodes. Most UWSN applications require constant data gathering and transmission due to the unpredictable underwater environment. There is a mechanism in traditional data gathering for delivering sensed data in a multihop fashion to a sink node [3; 15]. Autonomous underwater vehicles (AUVs) have been closely monitored to address this issue [9; 14]. Before returning home, the AUV gathers data from all sensor nodes and sends it to the destination (sink) node by using the shortest path tree (SPT) [16]. These underwater vehicles use actuators, sensors, and onboard intelligence to collect data without human interaction [17; 18]. The data collection procedure is the most critical aspect of UWSNs [19]. In recent work, data gathering with an AUV eliminates multihop transmission data, enhancing UWSN energy balancing [20]. To conserve sensor node transmission power, the AUV collects data from only a few nodes, known as gateway nodes (GNs) or route nodes, rather than visiting each node or cluster. The selection of GNs depends on the hello packets of received signal strength indicator (RSSI), node zones (closer nodes of AUV), and residual energy [21]. Member nodes (MNs) are associated with GNs by flooding data packets with the SPT method. When the GNs have consumed enough power, a new node with more residual energy from its neighbors is chosen as the gateway node. The selection of the AUV path is crucial because by using that path, an AUV can collect maximum data from nodes and lower energy consumption. If the AUV does not travel consistently, considerable packet loss and fluctuating energy usage may occur [22]. Recent research indicates that an elliptical-shaped trajectory is the best choice for effective data collection; maximum time is required to associate those MNs with GNs, resulting in a sizeable end-to-end delay. To overcome end-to-end delay with maximum data gathering problems, we have proposed the Elliptical Shaped Efficient Data Gathering (ESEDG) routing protocol. In ESEDG, an elliptical-shaped trajectory has been designed for AUV movement by considering the network area size. For data collection, all nodes have been categorized as member nodes (MN) and gateway nodes (GN). An MN makes a path with other MNs towards a GN by adopting a location-aware shortest path tree algorithm. ESEDG creates the maximum number of GNs and reduces the time required in the vertical association of the MN with the GN. Afterward, the AUV moves in an elliptical trajectory and collects data from the GN. The rest of the paper is formatted as follows: Section 2 contains related work; the problem is defined in Section 3. Section 4 describes the system model, trajectory design, and delay models. The comparison of the proposed ESEDG algorithm and results are discussed in Section 5. In Section 6, the conclusion and future work are discussed.