## 2 Related Work  
Underwater routing techniques are a hot research topic for UWSNs nowadays. There are several kinds of routing protocols that aim to improve energy efficiency, reduce end-to-end delay and prolong network lifetime [13; 14]. In this section, we provide a review on research works that have been done on this topic. Most energy-efficient routing protocols aim to reduce energy consumption and prolong network lifetime. A hierarchical routing algorithm called queen-bee evolution algorithm (QEGA) [15] works better in terms of energy consumption. QEGA has a high rate which results in premature convergence. Thus, the algorithm can find the optimal solution more quickly. However, QEGA does not consider the residual energy, which is important to extend network lifetime. The energy-saving vector-based forwarding (ES-VBF) protocol [16] defines a desirabeness factor based on residual energy and location information. In the routing pipe, nodes with more residual energy are more possible to forward packets. Although the algorithm prolongs the network lifetime, it needs the location information of all the nodes, which is still a challenge to be solved. The adaptive power controlled routing (APCR) [17] is an energy efficient routing schema that does not require any location information. In APCR, nodes are assigned to concentric layers according to the signal power of a received INTEREST packet broadcasted by sink nodes. Then, routing paths are decided based on layer numbers and residual energy. To improve the energy efficiency, nodes are able to adjust their transmission power to a set of values according to the information received during packet transmission. If forwarding nodes are found at multiple layers, the power is decreased. If no neighbor is found, the power is increased. Thus, APCR can achieve a high delivery ratio, but the number of forwarding nodes at each layer is not limited properly. If multiple nodes forward the same packet, the total energy consumption is increased. The Q-learning-based adaptive routing (QELAR) protocol is proposed [18] and Q-learning is proved to perform well in UWSNs in several aspects. QELAR defines the reward function based on the residual energy of the sensor nodes. In this protocol, sensor nodes choose the node with more residual energy as the next hop, so that the network lifetime of the network can be extended. However, in QELAR protocol, each node takes the responsibility to learn the environment by metadata exchanging and decide the next hop, leading to a higher energy consumption for each node. Moreover, the protocol does not restrict end-to-end delay. When the number of the sensor nodes increases, the routing will detour with more and more nodes, then the end-to-end delay is prolonged. Thus, QELAR works inefficiently in some situations because of the long delay. Many research works point out that the problems of latency in UWSNs are serious, especially for time-critical applications. In [19] the authors employ a probability model to describe the propagation delay of a link and select the next hop with lower delay. In [20], an underwater opportunistic routing (UWOR) is proposed. The forwarding set in which nodes can hear each other and prevent packet duplication is established. Each node in the forwarding set is assigned a relay priority which is related to the probability of successful transmission. The node with the highest priority and limited end-to-end delay can be chosen as the relay node. The simulation results show that UWOR can maximize good put while satisfying end-to-end latency requirements. However, it disables retransmission mechanisms, leading to a lower delivery ratio. Moreover, there are protocols that can jointly reduce energy consumption and end-to-end delay. Modified energy weight routing (MEWR) protocol [3] is energy efficiency guaranteed using a minimum algorithm. In order to determine an optimal path with a low end-to-end delay as well as low energy consumption, the cost of a link is formulated as a mixture of energy weight and delay weight.