DEDG: Cluster-Based Delay and Energy-Aware Data Gathering in 3D-UWSN with Optimal Movement of Multi-AUV  
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1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bin Abdulrahaman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 1  
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2 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China 2  
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1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bin Abdulrahaman University, P.O. Box 84428, Riyadh 11564, Saudi Arabia 1  
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3 Information Systems Department, College of Computer and Information Sciences, Princess Nourah bin Abdulrahaman University, P.O. Box 84428, Riyadh 11564, Saudi Arabia 3  
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4 Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Tagamburg, Russia 4  
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5 Department of Applied Probability and Informatics, Peoples' Friendship University of Russia 5  
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5 Department of Applied Probability and Informatics, Peoples' Friendship University of Russia 5
sensed data using acoustic signals. The sensor data are collected by the sink node, which is located over the on-shore or above the water level, for further analysis. In this way, UWSNs are supported for a variety of real-world applications, such as in aquatic habitat/health surveillance, underwater pollution level estimation, fishing and others [1; 2; 3; 4]. The sensed data are transmitted to the other end sink for analysis. Here, the sensor nodes are equipped with a limited amount of battery, which has to be saved in order to extend its lifetime. Clustering is an efficient solution for saving energy as well as for gathering the sensed data [5; 6; 7]. After creating clusters, a cluster head (CH) is selected, which is responsible for collecting data from its members. The CH is selected by evaluating any one of its potentialities, energy level, distance, node degree, moving speed, position, signal strength and others. These computations are formulated into fitness function using a bio-inspired optimization algorithm. Swarm optimization algorithms are involved in the process of fitness estimation [8; 9; 10; 11]. A UWSN becomes more flexible with the deployment of a moving autonomous unmanned vehicle (AUV). The AUV either collects data from individual sensors or from the CH. An AUV-aided UWSN mitigates energy consumption at the sensors by eliminating transmission of sensed data that use longer paths with n-hops [12]. The use of a AUV collects data directly from the sensors; this makes it possible to avoid the transfer of data through a chain of several sensors in a succession. Decreasing the number of transmissions efficiently balances the overall energy consumption and length of delay. Location prediction and path planning of the AUV are challenging since they impact on network lifetime. The route towards the AUV is selected for data transmission [13; 14; 15; 16; 17]. The trajectory of the AUV is fixed in some cases in which the moving path is pre-defined by the sink. This trajectory is also made dynamic by planning the movement of the AUV for data collection. The routing in a UWSN is majorly concentrated on the minimization of the energy and delay [18; 19; 20]. Network lifetime is improved by efficient path planning with evolutionary algorithms [21; 22]. Even though an AUV is appointed in a UWSN for data gathering, it is essential to plan the path properly. If a single AUV is used, it is easier to carry out path planning, but the coverage of the AUV over the entire network area still consumes more energy. So, more than one AUV is employed for performing data gathering with the aim of improving network lifetime.