###### Abstract
[PERSON] et al. proposed a distributed underwater clustering scheme (DUCS), which considers the residual energy of candidate nodes when selecting CHNs [19]. However, the distance between the candidate CHN and the SN is not considered in the DUCS algorithm. [PERSON] et al. came up with a clustering routing algorithm where the CHN selection is optimized by considering the remaining energy and the positions and the density of nodes [20]. Additionally, the mechanism of data transmission from the CHN to the SN is improved, thereby minimizing the energy consumption and maximizing the network lifetime. [PERSON] et al. presented a clustering routing protocol based on hybrid multiple hops, where the CHN selection based on the remaining energy of nodes is self-organized and the path from CHN to the destination node is obtained by the establishment of a minimum spanning tree [21]. This algorithm can reduce energy consumption and extend the network expectancy, but is designed for TWSNs instead of UWSNs. [PERSON] et al. designed an adaptive clustering underwater network (ACUN) algorithm, which considers the residual energy of nodes and the energy loss of paths to select CHNs [22]. It also considers the node energy condition to select paths with high energy efficiency. However, the distance factor has not been considered in this literature. [PERSON] et al. proposed a cluster-based underwater wireless sensor network (CUWSN) algorithm, which selects CHNs based on the residual energy of nodes and adopts multi-hop transmission to forward data packets to the destination node [23]. The CUWSN can reduce energy consumption and improve the performance of the network, but the distance factor has not been taken into account and the multi-hop paths have not been optimized. In [24], [PERSON] et al. did a survey on routing algorithms in UWSNs, which aimed to solve problems such as data transmitting and node deployment, as well as localization. In the survey, the authors analyzed and compared several clustering routing algorithms such as the DUCS [19], the distributed minimum cost clustering protocol (MCCP) [25], temporary cluster-based routing (TCBR) [26], the location-based clustering algorithm for data gathering (LCAD) [27], and the multipath virtual sink architecture [28]. The MCCP was proposed by [PERSON] et al. in [25], which addressesthe hotspots near the SN and balances the traffic load. In addition, the MCCP determines the number of CMNs according to the locations of the CHNs and the SN. However, the multi-hop method is not supported in the MCCP and the period of re-clustering is too long. TCBR was presented by [PERSON] et al. in [26], where multiple SNs are placed on the water's surface in order to solve the problem that nodes near the SN consume more energy and die prematurely. TCBR can balance the energy dissipation, but it cannot achieve high efficiency in time-critical applications. The LCAD was given by [PERSON] et al. in [27], where horizontal acoustic communication is employed when CMNs transmit data to CHNs, and autonomous underwater vehicles (AUVs) are used when CHNs send data to the SN. The LCAD can solve the energy hole problem and reduce energy dissipation. However, it relies on the network structure and its effectiveness is affected if the node mobility is considered. The multipath virtual sink architecture was proposed by [PERSON] and [PERSON] in [28], where the aggregation nodes aggregate the data from other nodes in the same cluster, and then transmit the aggregated data to the SNs. The authors assume that these SNs can achieve high-speed communications so that they form a virtual SN. This method can guarantee high reliability, but the duplicate data packets result in redundant transmission, which increases the resource consumption. A pressure routing algorithm for UWSNs was presented by [PERSON] et al. in [29], which employs anycast routing to send data to the SN according to the pressure levels.