## 1 Introduction
studied the problem of trust between nodes in UANs, analyzed the existing trust management mechanism, and proposed a trust cloud model according to the characteristics of UANs [11]. The trust cloud model can improve the calculation accuracy of trust between nodes and the communication success rate. In [12], [PERSON] et al. proposed a scheme to select the trusted cluster heads and their member nodes based on fuzzy logic. The proposed scheme uses a lightweight XOR encryption algorithm to authenticate the nodes in the cluster and calculates the trust value based on the residual energy of the node, the distance, and relative mobility between nodes, and then uses fuzzy logic to select trusted nodes based on the trust value. In both [11; 12], trust management is used to improve the reliability of transmission. Inspired by [11; 12], in this paper, we propose a fuzzy logic-based trust evaluation mechanism (FLTEM) for underwater sensor nodes. FLTEM uses the fuzzy comprehensive evaluation decision model to calculate the comprehensive trust value of nodes to identify MNs and improve the reliability of the network. Moreover, we designed a trust-aware and fuzzy logic-based reliable layering routing (TAFLRLR) protocol using FLTEM. We employ a fuzzy control system (FCS) to calculate the forwarding probability (FP) and select the best next-hop node. The main contributions of this paper can be summarized as follows:  
1. A layering algorithm is proposed to configure a layer for each node according to the minimum hop count, which improves the delivery rate of packets by avoiding the problem of void area. A fuzzy logic-based trust evaluation mechanism for underwater nodes, called FLTEM, is proposed. Considering the residual energy and different forwarding behaviors between HELLO packets and DATA packets, a fuzzy comprehensive evaluation decision model is introduced to calculate the direct trust value (DTV) of a neighbor node, and then the indirect trust value (ITV) is calculated according to the DTVs of the public neighbor nodes. Finally, the comprehensive trust value (CTV) can be obtained based on the DTV and ITV, and CTV can be used to effectively identify the MNs in the network. For instance, node n calculates the \(DTV_{n,a}\) for its neighboring node a based on the communication behavior of node a, node a calculates the \(DTV_{a,k}\) for its neighboring node k based on the communication behavior of node k; thus, the ITV of node k for node n is \(DTV_{n,a}\times DTV_{a,k}\). The CTV is the weighted sum of DTV and ITV. The calculation of DTV, ITV, and CTV is detailed in Section 4. 3. A trust-aware and fuzzy logic-based reliable layering routing protocol, called TAFLRLR, is proposed to achieve transmission reliability. TAFLRLR takes the CTV and node density (ND) of a candidate node as the input of FCS and the FP as the output. The candidate node with the maximum FP will be selected as the best next-hop node. 4. Simulation results in NS3 show that the TAFLRLR protocol has superior performance in terms of transmission reliability, routing reliability and energy efficiency. The remainder of this paper is organized as follows. Section 2 presents several common threats and attacks faced by UANs. The network model and network initialization are introduced in Section 3. The proposed FLTEM and TAFLRLR are described in detail in Sections 4 and 5, respectively. In Section 6, we evaluate the performance of the TAFLRLR protocol via NS3 simulations. In Section 7, the paper is summarized, and future research is proposed.