## 2 Related Work and Motivations  
This section provides a brief overview of the existing data routing approaches designed to investigate the underwater environment [31; 32]. Different techniques like probabilistic scheme [19], depth-based scheme [22], and AUV-based scheme [21; 28; 30] are designed to collect data in the literature of UAN. A few of them bring up the idea of a courier FN [9] to decrease the data load on-forward FNs. A localization-based data gathering technique is investigated in [17] to decrease the network energy load for UANs. They divided the network into different depth levels. The FNs with a higher depth level send their data packets to the FNs belong to the lower depth level in the form of a chain. FN residual energy is taken as a routing constraint for the data forwarding procedure. In [21], the authors introduced a depth-based data gathering approach. In this model, they minimized the end-to-end data delay by reducing network throughput. Moreover, the forwarder FN selection is also based on a maximum number of neighbor FNs to avoid the data error, the data loss, and the energy hole in the network. The authors in [5] discussed a directional flood-based data gathering approach. The focus of this method is to check the quality of the links between the FNs that are taking part in the data flooding process [30]. If the quality of the link between participating FNs is poor, authors involve some other FNs to participate in the flooding process. This approach achieves reliability in data delivery at the cost of additional energy. Redundant data consumes the network resources and deteriorate the network performance by increasing the congestion. Due to the rapid increase of internet data, many data redundancy techniques have been introduced in recent years [12; 13; 14]. Many current techniques provided suitable solutions to improve the network performance by removing the data redundancy in the network. It has been broadly agreed that data redundancy eradication offers great benefits in practice. Generally, the benefit of removing the data redundancy is the improved network performance in terms of higher network throughput and lower end to end delay [12; 13; 14]. However, the currently provided solutions are not so effective and remove a part of the original data with redundant data. [PERSON] and prior in [18; 20], investigated and analyzed the effect of deep and shallow wavy water on the energy consumption of the network. They engaged three types of data transmission links to observe energy consumption. These are direct transmission link, cooperative transmission link, and the cluster-based transmission link. From their experimental results, they found that the direct transmission link achieves very poor outputs in the underwater scenario. As the distance between two communicating FNs increases, data drop rate also increases due to the interference in the acoustical channel which badly affects the overall network throughput [18]. Cooperative transmission overcomes this issue and outperforms in deep wavy water. Relaying is effective to save the network energy resources, however, the cooperative communication increases the complexity level of the network. While, in the cluster-based transmission, both cooperative and direct transmission is involved. Firstly, direct transmission is utilized between member FNs and the head node to collect data. After that, the cooperative transmission link is employed for forwarding the data from the head node towards the sink. The cluster-based transmission reduces the energy consumption for member FNs [4; 15; 16]. However, the cluster-based transmission creates the problem of the rapid battery drainage for cluster heads, which can be solved by using the MS or the AUV. The AUVs alleviate the energy burden by forwarding the data of the cluster heads. In AUV-based approaches [21; 24; 25; 26], the authors consider a 3D UAN, where they keep the depth level of all the FNs same as the FNs are anchored to the floor of the ocean.