## 2 Related Work  
Compared with traditional WSNs, UWSNs, which uses an underwater acoustic channel for transmission, consumes more energy [21]. If the node runs out of energy, it will immediately fail, which will cause network coverage vulnerability and greatly affect network performance. [PERSON]. [22] designed a microbial battery for sensor to solve the problem of limited energy. However, this way of collecting energy is affected by the environment, although it increases the survival time of nodes, it increases the cost. Therefore, saving sensor energy by controlling the behavior of nodes in UWSN is still a convenient and efficient way. The routing algorithm PER (power-efficient routing protocol) and SEANAR (energy-efficient and topology-aware routing protocol) were compared in [23]. Both algorithms add location information and residual energy information of underwater nodes when selecting the next hop. PER algorithm consumes less energy and reduces the number of repeated packets sent, but it is prone to communication blanks and reduce network coverage. Based on topology control, SENAR finds and selects the next forwarding node in two stages, but the node near the sink has high energy consumption and is prone to energy holes. [PERSON] [24] et al. proposed a reliable and energy-efficient pressure-based routing (RE-PBR) algorithm. Considering the link quality, depth and residual energy, the information acquisition and routing and forwarding algorithm are designed to balance the energy consumption and make the routing protocol more efficient and energy-efficient. Because the algorithm does not consider the location of nodes, it will lose advantages in the perception and coverage of regions. Considering the complexity of underwater environment, network stability, anti-interference and end-to-end delay must also be considered in the study of UWSNs [25]. In [26], an adaptive disjoint path vector algorithm suitable for heterogeneous wireless sensor networks was proposed. The algorithm has the characteristics of energy perception and distributed fault tolerance, including initial and recovery stages. The scheme can maintain the connection between nodes, but the advantage of optimizing energy consumption of nodes is not obvious. The CLTC (Cluster-Based Topology Control) framework has been improved in [27]. First, the nodes were divided into clusters by k-means algorithm, and then topology control between clusters was implemented by using relative neighborhood graph. The greatest advantage of this algorithm was that it could maximize the network life cycle, but it offered no clear advantage in reducing inter-node interference. A method to solve the energy optimization problem of wireless sensor network was proposed in [28]. This method also involves topology control and multicast technology based on network coding. Compared with using them separately, this method improved overall performance. The scheme reduced end-to-end delay and optimized network energy consumption, but was slightly insufficient in network connectivity and coverage. In [29], a basic theoretical algorithm based on WSNs was proposed. Topology control techniques of planar network and layered network were discussed respectively, and algorithms was further classified according to the key parameters of state scheduling. However, this algorithm has many shortcomings and needs to be further research. Considering the two factors of interference and delay in topology control, three optimization goals were proposed in [30], maximum interference, average interference, and average path interference. A centralized algorithm with a greedy strategy was proposed that satisfied the delay constraint while minimizing interference as much as possible. Then, a distributed algorithm was proposed to reduce average interference. To minimize average path interference, a local delay constraint Bellman-Ford algorithm was also put forward. Finally, the network topology consisted of these optimal paths.