## 2 Related Work  
Trust models have been widely used in WSNs, but the research on trust models suitable for UWSNs is still relatively small. From the existing research results, trust models can be used in areas such as malicious node detection [8], data collection security [9], and intrusion detection [10]. [PERSON] et al. [7] proposed a trust-aware dynamic routing algorithm augmented with an AODV protocol (TADR-EAODV) protocol for secure communication in WSNs. It considers the trustworthiness of nodes while making routing decisions. It dynamically adjusts the routing paths based on real-time network conditions such as node availability, energy levels, and security factors. Improvements are made in detecting defective nodes and average packet transmission rate. GEDAR is geographic and opportunistic routing protocol. It selects those nodes with the highest Expected Packet Advancement (EPA) as forwarders. The GEDAR protocol solves void routing by adjusting the depth of void nodes. However, node movement may not always be optimal and may consume more energy. [PERSON] et al. [11] proposed a cluster-based vector-based forwarding (CVBF) technique to improve the effectiveness of vector-based forwarding protocols (VBFs) [12]. The entire UWSN volume is divided into multiple rectangles to make clusters, and then a virtual receiver is assigned to each cluster in CVBF. [PERSON] et al. [5] extended VBF to the HH-VBF protocol (hop-by-hop VBF protocol). HH-VBF changes the direction of the forwarding pipe by hopping hop-by-hop throughout the lifecycle. Although network performance can be improved by dynamically changing the direction of the flooding pipe compared to VBF, HH-VBF has lower performance in sparse networks when a smaller pipe radius is set. [PERSON] et al. [13] proposed a Q-learning-based dynamic network topology aware routing algorithm (QTAR) which uses implicit tangent vertex identification to optimize the selection of the NF (next forwarder), thereby mitigating the energy wastage incurred in forwarding packets away from the receivers and improving network performance. [PERSON] et al. [14] proposed a trust model RFSN for WSN. In RFSNs, each node monitors the communication records of other nodes and maintains the trust of other nodes locally. [PERSON] et al. [15] proposed a trust model which defined seven categories of trust evidence. However, it is difficult to assign reasonable weights to all trust indicators when fusing multiple pieces of evidence. [PERSON] et al. [16] proposed an unattended WSN trust model based on subjective logic. However, their main focus was ensuring the security of observational evidence storage, so they did not deal with trust assessment and trust update. [PERSON] et al. [17] proposed a trust model called Attack-Resistant Trust Model based on Multidimensional trust metrics (ARTMM) in UWSNs. This scheme can reduce the impact of the underwater environment to some extent. However, ARTMM ignores the effect on the network layer after an attack, and it is difficult to accurately describe the trust relationship using only fuzzy logic. [PERSON] et al. [18] used fuzzy logic and cryptographic encryption schemes considering relative mobility, energy, and distance metrics to select secure cluster head nodes and transmit data within the cluster, but their transmission did not consider the energy efficiency and low latency of the forwarding path. [PERSON] et al. [19] proposed a trust model based on reinforcement learning for UWSNs. The scheme reduces the influence of environment on the trust model to a certain extent and utilizes the reinforcement learning theory to update the trust dynamically, which can cope with changing attack patterns to a certain extent. Transmission security models for UWSNs mainly rely on the introduction of cryptographic algorithms and routing protocols that are unable to cope with attacks from within the network. The introduction of a trust model to regulate internal nodes is an effective way to deal with internal attacks.