## 2 Related Work  
There are many issues impeding the optimal deployment of UASNs for data acquisition in the field of marine environmental monitoring and surveillance. Due to a large amount of absorption and scattering losses, underwater applications mainly use acoustic communications instead of radio communications. Inevitably, the utilization of acoustic channels presents several challenges in data collection due to long propagation latency, narrow bandwidth, and a high bit error rate. Therefore, multi-hop transmission techniques from normal nodes to a pre-deployed sink have been preferred methods applied to UASN data collection [12; 13]. Some sensor nodes in multi-hop networks are selected as relay nodes to forward packets from other nodes that are far from the sink. These relay nodes exhaust their energies more rapidly than the other nodes, which leads to extremely uneven energy consumption. In addition, the multi-hop communications of large-scale networks usually lead to excessive consumption of energy. Additionally, due to the limitation of acoustic communication in UASNs, it is challenging to ensure network connectivity, so traditional data collection methods based on multi-hop communication often fail. To address the problem of link disconnection caused by water current, [PERSON] et al. proposed a data collection scheme to repair the broken link by using controlled mobile nodes as relay nodes in [14]. If the distance between two neighboring nodes is larger than a threshold, the sink node will guide the mobile node to the central location of the corresponding link to recover the broken link. This scheme can efficiently address the disconnected network problem caused by the specific underwater environment, however, the issues of large scaled networks and uneven energy consumption were not addressed in this proposal. Next, [PERSON] et al. in [15] investigated a geographical location-based clustering algorithm LCAD for data collection in 3D UASNs. According to the optimal horizontal and vertical transmission range derived experimentally, they divided the entire network into 3D grids that were \(30\times 40\times 500\) m\({}^{3}\) in size. Each grid forms a cluster with a single cluster-head. The cluster-head nodes collect data from their cluster member nodes and then transfer the collected data to the sink by inter-cluster multi-hop transmission. This scheme can effectively reduce the energy consumption of normal nodes, but it does not address the overuse and the energy earlier exhausted problems of the cluster head nodes nearest the sink node. Similarly, [PERSON] et al. in [16] presented an efficient clustering routing protocol in which data is transmitted to a destination node with a single hop. Though clustering is an effective method to optimize the total energy consumption in large-scale networks, the issue of uneven consumption of energy remains a problem for cluster-heads, which increases the need for a mobile element to collect data from neighborhoods in UASNs. [PERSON] et al. in [17; 18] designed a mobile sink-based model for data collection in UASNs and analyzed the network performance indicators of this model, including energy consumption, packet delivery ratio, network lifetime, and message delay. The results of analysis and simulation showed that the mobile sink auxiliary data collection for UASNs can effectively improve the data delivery ratio and optimize node energy usage to prolong the lifetime of UASNs. In [19], [PERSON] et al. proposed the AUV-aided routing protocol AEDG for reliable data collection in UASNs. They used a Shortest Path Tree algorithm to gather data of nodes to gateways, and then utilized an AUV to collect the data from the gateways. Though this model can effectively balance energy consumption and prolong lifetime of network, in their methods, the communication radius is presumed to be deterministic, although in practive, data transmission in UASN, is probabilistic and degrades with distance. Recently, in [20] and [21], [PERSON] et al.