Some research has been conducted on trust evaluation in edge computing [5; 6; 7]. [PERSON] et al. [8] took multi-dimensional trust data as the training set of a BP neural network to evaluate the trust degree of edge nodes in a computational power network, and adopted the improved particle swarm optimization algorithm to optimize the structure and weight of the neural network so as to effectively improve the detection rate of malicious nodes. This method adaptively adjusted the weight of trust aggregation. Particle swarm optimization was used as feedback to realize the incentive mechanism for edge nodes. However, it did not take into account privacy protection and computational costs. It was also not tested for anti-aggression. In the literature [9], the decision tree classification model method was used to construct trust rules, and the Euclidean distance concept was used to calculate the trust value in the calculation of recommendation trust. Furthermore, the artificial neural network function was used to self-train the vehicle nodes that did not meet the expected trust value to improve the vehicle trust degree. This work developed a manual training model as an incentive mechanism. However, it did not consider privacy protection and anti-aggression. It also did not take into account computational costs. In the literature [10], a capsule neural network was used to predict trust attributes based on historical data to obtain the trust value of edge devices. Only the shallow network structure and a small amount of training data were needed to realize the trust evaluation, which met the time requirements of frequent processing of multi-modal and small-sample data in edge scenarios. This study used a capsule neural network to aggregate trust attributes and overcome the subjectivity of trust weights. However, it did not consider privacy protection and incentives. It also did not analyze adaptability to attacks and did not consider the computational cost of the algorithm. [PERSON] et al. [11] proposed a credible edge platform by combining blockchain with edge computing. This realizes the lightweight design of the platform through the microservice architecture. It improves the portability of the platform by introducing the Edgex Foundry framework, and it also verifies the availability of the trusted edge platform by deploying it on multiple network nodes for simulation. [PERSON] et al. [12] used multi-weight subjective logic to realize the credit value calculation of the reputation system and construct the local and global reputation. They determined the weight of reputation according to the three aspects of familiarity, similarity and timeliness, and constantly updated the value of reputation. This method improved the efficiency of resource allocation. However, it did not take into account privacy protection and robustness against attacks. It also did not take into account inter-transmission incentives. At the same time, the establishment of weight in reputation calculation was subjective, which reduced the accuracy of trust values. [PERSON] et al. [13] introduced trust  
Figure 1: Cloud-Edge-End Collaboration architecture. chains when evaluating trust of sensor nodes. The trust value of a single atomic trust chain combined interaction trust, energy trust and recommendation trust. The fine-grained trust values of sensor nodes were evaluated by serial and parallel trust transitions in the merged trust chain. This approach was robust against malicious attacks. However, it did not consider privacy protection and incentive of trust data. The computational cost of the algorithm has not been evaluated. It also did not discuss the weight of trust aggregation, which reduced the accuracy of trust values. In the literature [14], node trust was evaluated according to several parameters. Direct trust evaluation considered node communication success rate, proximity, packet loss rate and residual energy. In the evaluation of indirect trust, recommendation trust with different weights came from the surrounding nodes.