## 2 Related Work and Motivation  
Due to varying underwater conditions, recent data gathering and network layer protocols face many challenges [12, 13]. Some of the existing proposed schemes use GNs to collect data from MNs and transmit it to the MS, where nodes with relatively high received signal strength indicator (RSSI) values are selected as GNs [14]. In [15], the authors describe various factors affecting the underwater communication environment. They investigate the characteristics of the underwater acoustic channel and its impact on the data link and network layers. The performance of autonomous underwater robotic systems (AURS) greatly depends on reliable and efficient communications. A short communication range makes the terrestrial electromagnetic and optical communication techniques infeasible in an underwater environment. Thus, acoustic technology is invoked as the most appropriate technology in the harsh and unpredictable conditions of the ocean. Despite their merits, acoustic signals suffer from time-varying multi-path fading, Doppler shift and strong attenuation at high frequencies. In [16], the authors propose a new mathematical technique to find the distance between nodes in a UWSN. This mathematical scheme relies on the RSSI value. In their work, a hybrid computation technique is introduced, which inverts the transmission loss using the Lambert W function (calculated by the Halley method). The authors in [17] propose a round-based clustering scheme for data redundancy resolving (RBCDRR). In this research work, the authors propose a cluster-based approach to deal with data redundancy. The cluster heads are selected on the basis of the nodes' residual energy and relative communication distance from the base station. In order to reduce the data redundancy, data aggregation with Euclidean distance is applied in the cluster. Every time the cluster head changes, new inter-cluster and intra-cluster communication is set up. The basic ant colony optimization algorithm (ACOA) is a heuristic algorithm that is not only robust, but can also easily be combined with other algorithms. However, convergence at the local solution is its major disadvantage. On the other hand, the artificial fish swarm algorithm (AFSA) quickly converges at the global solution; however, it has lower precision while finding the global solution. Thus, the authors in [18] combine AFSA and ACOA in a self-adaptive manner while searching for an optimal routing path. The proposed ACOA-AFSA routing algorithm possesses the advantages of ACOA and AFSA, such that both the transmission delay and energy consumption of the network are minimized. In [19], the authors present a routing protocol for UWSNs. This protocol aims to maximize data collection and to overcome the energy hole problem. Initially, the network area is divided into different zones, such that the AUV moves on a pre-defined path for data gathering. Subject to saving scarce energy resources, the authors use sleep and awake mechanisms in the respective zones. In UWSNs, the major challenges are inefficient data gathering, minimum network lifetime, continuous variation in topology and high energy consumption cost during data transmission. Different routing techniques (in UWSNs) have been proposed to tackle the above-mentioned challenges. These techniques use the static sink, as well as the MS approach. In the latter approach, the AUV traverses the network and collects data from each GN for efficient data gathering and network lifetime maximization. However, these techniques do not devise a criterion to limit the number of associated MNs with their respective GNs, so that the high data loss and increased energy consumption of GNs can be minimized. In the AUV-aided underwater routing protocol (AURP), fixed GNs are used to collect data from the MNs [20]. Therefore, GNs consume high energy in relaying excessive data. The quick energy depletion of GNs leads to short network lifetime and also results in a low data delivery ratio at the sink.