## 2 Related Work
### The Clustering Routing Protocol  
This section presents related works on clustering routing protocols and discusses the difference between them and our proposed protocol. The earliest one is the low-energy adaptive clustering hierarchy (LEACH) protocol that uses a probabilistic method to select CHNs, but the remaining energy of nodes is not considered [27]. This makessome selected CHNs die too early, which affects the balance and efficiency of the network energy. Moreover, the LEACH does not support the multi-hop transmission mechanism. Therefore, researchers proposed the improved clustering routing protocols based on the LEACH. [PERSON] et al. optimized the LEACH based on expected residual energy (LEACH-ERE), which adopts an improved CHN selection scheme based on the LEACH protocol, employs energy predication, and distributes the network load evenly in order to extend the network lifetime [28]. [PERSON] et al. presented a partitioned-based and energy-efficient LEACH (PE-LEACH) protocol that divides the whole network into quadrants, which is energy-efficient and fault-tolerant [29]. In addition, the CHN selection scheme and the data transmission process are improved in PE-LEACH protocol. However, the protocols in [28] and [29] are designed for TWSNs, and they should be modified for UWSNs. [PERSON] et al. adopted an energy-efficient grid routing based on 3D cubes (EGRCs) for UWSNs, where the network is divided into lots of small cubes and each cube is regarded as a cluster [30]. What is more, the EGRC protocol optimizes the CHN selection and improves the search process for the next-hop node. However, the EGRC does not present the detail of the data fusion mechanism as the data redundancy may exist and should be reduced. In [31], an underwater clustering protocol on the basis of the fuzzy c means and the moth-flame optimization (FCMMFO) was proposed to enhance the performance of UWSNs. In the FCMMFO, the optimal number of clusters is determined by using the fuzzy c means and the appropriate CHNs are selected by the moth-flame optimization. Nevertheless, a multi-hop mechanism is not provided in the FCMMFO. [PERSON] et al. presented an energy-efficient underwater clustering protocol based on the fuzzy scheme and particle swarm optimization (FBCPSO) in [32], where the fuzzy scheme and particle swarm optimization are used to form clusters and select CHNs respectively, which can balance and reduce the energy dissipation. However, the multi-hop routing mechanism and the data fusion method are not considered by the authors. [PERSON] et al. put forward an underwater clustering scheme based on the magnetic induction for UWSNs [33], where the Voronoi diagram is employed to form clusters and the jellyfish breathing process is used for CHN selection. This scheme can achieve the high energy-efficiency and prolong the network lifetime. However, the multi-hop routing path has not been optimized in [33]. [PERSON] et al. introduced an underwater clustering protocol according to redundant transmission control (RTC), which eliminates the data redundancy at the CHN level and at the region head level [6]. Moreover, the authors presented a dynamic CHN rotation method, which can balance the energy consumption and improve the reliability of the network. However, this scheme relies on a mobile SN that moves from the surface to the bottom to collect data. [PERSON] et al. presented an underwater clustering-based localization protocol [34], where the CHNs perform localization procedure rather than the whole cluster. Furthermore, the retransmission control mechanism is carried out to control unnecessary transmission, which can reduce energy dissipation. Nevertheless, this protocol does not consider the data redundancy that affects the energy consumption. [PERSON] et al. presented an underwater adaptive clustering routing scheme [35], where the CHNs perform the data fusion in order to decrease the energy loss. Moreover, the competition radius of nodes is decided based on the distance factor and the residual energy of nodes.