## 2 Related Work  
Data forwarding for underwater wireless sensor networks has drawn a lot of attention in the past decade. There are several kinds of routing protocols that aim to improve energy efficiency, timeliness and adaptability to node mobility of UWSNs. In this section, we review the related work on this topic. [PERSON] et al. have pointed out the urgent need and significance of UWSNs [1; 2]. To satisfy the demand of timeliness of UWSNs, a lot of research was dedicated to decreasing the latency of data forwarding. [PERSON] et al. [6] devised a forwarding method named Multi-modAI Reinforcement Learning-based RoutlNg (MARLIN) protocol. The MARLIN strategy selects the best relay node along with the best communication channel, and it can be configured to seek reliable routes to the final destination, or to provide faster packet delivery. [PERSON] et al. [3] proposed a Greedy and Adaptive AUV Path-finding (GAAP) heuristic. The GAAP strategy proposed a heuristic algorithm which aims to find the path of the AUV so that the value of information of the data delivered to sink nodes is maximized. It showed that the GAAP strategy delivers much more value of information than Random Selection (RS), Lawn Mower (LM) and Traveling Sales Man (TSP) strategies do. Nevertheless, the advantage of the GAAP strategy over the TSP strategy decreases with the network size which enables TSP strategy to collect more packets, and the average end-to-end delay of GAAP strategy is higher than TSP strategy. Meanwhile, many energy-efficient forwarding methods are devised to prolong the network lifetime. [PERSON] et al. [7] proposed a \(Q\)-Learning-based Energy-Efficient and Lifetime-Aware Routing (QELAR) Protocol for Underwater Sensor Networks. QELAR adopted \(Q\)-Learning algorithm which defines the residual energy of sensor nodes as the reward function. Therefore, in QELAR protocol, sensor nodes select the node with the most residual energy as the relay node, thus the network lifetime can be prolonged. However, QELAR did not constrain the end-to-end delay, which resulted in longer delay when the number of sensor nodes was increasing. [PERSON] et al. [8] devised an Energy Balancing Routing (EnOR) Protocol for Underwater Sensor Networks. The EnOR protocol adopted the idea of balancing the energy consumption among neighboring nodes in the forward set by rotating the priority of them so as to extend the network lifetime. However, a large candidate set results in high delay because the link quality of the high priority nodes is usually low given the long distance between the sender and the high priority nodes. In addition, [PERSON] et al. [9] proposed a \(Q\)-Learning-based Delay-Aware Routing (QDAR) Algorithm to Extend the Lifetime of Underwater Sensor Networks. It took both timeliness and energy efficiency into account by defining delay-related cost and energy-related cost. Moreover, several studies of mobility of sensor nodes dealt with topology changes due to node mobility. For instance, [PERSON] et al. [10] proposed an Opportunistic Forwarding Algorithm based on Irregular Mobility (OFAIM). OFAIM aims to maximize the network delivery ratio of UWSNs in a 3-D mobility model due to irregular movement. However, there are only sensor nodes but no sink nodes in the scenario of OFAIM, and no descriptions of how the data will be retrieved from underwater sensors. Additionally, there are some approaches that reduce energy consumption in consideration of node mobility. [PERSON] et al. [11] proposed a Role-Free Clustering with \(Q\)-Learning (CLIQUE) for WSNs, which determines the selection of cluster heads without control overhead. The number of hops to reach mobile sink nodes and the residual energy of sensor nodes are jointly adopted as the reward function, thus enhancing the energy efficiency. However, CLIQUE assumed that sensor nodes uniformly disseminate data without consideration of the limited storage of sensor nodes. [PERSON] et al. [12] invented a clustering protocol for UWSNs based on the mobility model proposed by [PERSON] et al.