Finally, the trust was aggregated to obtain a multi-dimensional comprehensive trust value. This method could resist malicious attacks and balanced the energy consumption of nodes. However, it did not consider privacy protection and incentives. In the calculation and aggregation of direct and indirect trust, it did not analyze the specific allocation of trust weight. [PERSON] et al. [15] collected trust values from multi-source evaluation from the perception level, and assigned weights to different types of trust evaluation values according to service attribute sets, self-service evaluation factors, user quality evaluation factors and social sensor personality preferences so as to achieve multi-dimensional aggregation of trust values. This method considered the adaptability of trust weight and reduced the computational cost. However, it did not consider privacy protection and robustness against attacks. It also did not consider the incentive mechanism between sensor nodes. Fuzzy logic constructs multiple fuzzy factors by modeling the uncertain factors, deducing the uncertainty relation of entity behavior through fuzzy operators, evaluating the subject using the fuzzy comprehensive evaluation algorithm and, finally, obtaining the result. Some examples from the literature [16; 17; 18; 19; 20; 21] introduce fuzzy logic technology to implement trust management in edge computing. In the literature [16], prior knowledge in trust evaluation was provided by a fuzzy inference system, which combined expert knowledge of trust evaluation into a fuzzy rule base using fuzzy methods. It was also based on artificial neural networks to capture different patterns to make decisions about the trust status of IoT nodes. In the literature [17], the uncertainty in trust evaluation was divided into three fuzzy sets: packet loss factor, false packet injection factor and content change factor. The algorithm based on fuzzy logic was used to evaluate the trust degree of vehicles, and the network topology factor was introduced to combat data change attack. This method could resist packet loss attack, error injection attack and data change attack. It also solved the uncertainty of trust weight by aggregating trust factors through fuzzy logic. However, it did not consider privacy or incentives. It also did not estimate the computational cost. [PERSON] et al. [18] reduced security threats by establishing a credible environment based on ambiguity in smart cities. The model could effectively avoid and isolate malicious nodes in IoT, and reduced the impact of collusion attacks. However, the above work is one-sided in the extraction of trust data when constructing fuzzy factor subsets, which will affect the accuracy of trust evaluation values. In order to improve service quality, [PERSON] et al. [19] designed the collaborative task offloading scheme FCTO by taking the delay sensitivity of QOS as the fuzzy input parameter. This scheme optimizes the task completion time and server utilization by introducing fuzzy logic in task offloading. Due to the selfish behavior of edge nodes, the quality and reliability of their trust evaluation in complex and changeable network environments are affected. An incentive mechanism can be introduced to reward the quality of trust evaluation results so as to improve the initiative of edge nodes in trust evaluation with limited resources. The incentive mechanism in trust evaluation is the direction of future research. In recent years, some research has taken incentive into account. In the literature [22], smart contracts were used to automatically punish the misconduct of stakeholders. Additionally, decentralized accountability mechanisms and automatic rewards were developed to encourage nodes to verify collaboration services and provide verification rewards. This regulated the behavior of participants and built a reliable edge computing network.