## 1 Introduction  
Underwater Wireless Sensor Network (UWSN) technology is receiving widespread attention both at home and abroad and is being widely used for marine data collection, pollution prediction, offshore mining, shipwack avoidance marine monitoring, and more. UWSNs are one of the key basic support technologies for the development of intelligent water conservancy and ocean exploration, and have broad application prospects in real-time water quality monitoring, water pollution control, and other aspects. However, UWSNs have many limitations compared with wireless sensor networks due to the specific conditions:  
* Acoustic communication: Due to the large attenuation of electromagnetic waves and light waves underwater, hydroacoustic communication is the main means of underwater long-distance communication [1]. * Bandwidth: Acoustic waves travel underwater at 1500 m/s, which results in much lower bandwidth for acoustic communications compared to terrestrial wireless sensors (TWSNs) [2]. * High BER: Hydroacoustic channels are characterized by high time variation, long propagation delay, high signal attenuation, high environmental noise, and low available bandwidth, which, to a certain extent, result in high BER and high packet loss, affecting the efficiency and accuracy of data transmission [3]. * Environmental constraints: The harsh underwater environment constrains the computational resources and storage resources of UWSNs nodes. Underwater sensor nodes (UW-sensors) use batteries for their energy supply, and it is technically difficult to recharge the batteries of UW-sensors at a higher cost. In addition, due to environmental noise, channel fading, and so on, UW-sensors need to consume more energy to compensate for the signal during data transmission, which greatly increases the energy consumption of the UW-sensors [4]. All these factors converge to result in minimal resources available for UWSNs. They also limit the prospect of using some of the more mature security algorithms used in TWSNs in UWSNs. Due to the limited transmission range, UW-sensors usually relay the received data to the receiver nodes through multiple forwarding nodes, which provides conditions for malicious nodes (MNs) inside the network to damage the underwater wireless sensor network. MNs within the network can forge, discard, eavesdrop, and modify the transmitted data to affect the accuracy and efficiency of network data transmission. In addition to this, MNs can send a large number of useless and repetitive messages inside the network to occupy the network channel, consume a large amount of energy, and greatly reduce the survival time of the UWSNs. Numerous researchers have introduced trust models into UWSNs to identify MNs inside the network and drive them out of the network. This is a core element. The trust model assigns a trust value to each underwater sensor node, and other nodes confirm their trustworthiness before collaborating with them. The trust model can be applied to protocols such as routing protocols, localization, and synchronization to enhance the security of the UWSNs. Unlike authentication models, trust models usually work periodically to collect trust evidence and calculate the trustworthiness of nodes through their multifaceted performance. The trust model can effectively respond to internal attacks and improve network security. We designed a new trust management scheme for UWSNs to ensure the security of packet transmission and improve the efficiency of data transmission. The main contributions of this paper are shown below:  
* We propose a trust model for checking the trust performance of underwater nodes in UWSNs. The proposed scheme considers three metrics (environmental trust, direct trust, and indirect trust) to analyze the performance of UW-sensors, identify MNs, and find the set of forwarding nodes suitable for data transmission.