## 2 Materials and Methods
### Lightweight Fuzzy Collaborative Trust Evaluation Model  
In order to effectively evaluate the trust value of edge devices in the CEEC environment, we design a lightweight fuzzy cooperative trust evaluation model (LFCTEM). In this paper, four trust factors generated by fuzzy sets are used to fully model the neighboring cooperation attributes of edge devices and the trust value of edge devices is evaluated based on the fuzzy logic algorithm. The fuzzy logic system is used to overcome the indirect uncertainty of the trust mechanism. In the CEEC environment, a group of neighboring edge devices is used to evaluate the trust value of one of the edge devices so as to further improve the reliability of the trust evaluation value. However, due to the complexity of the CEEC environment, the unreliability of wireless communication transmission and the unpredictability of edge device behavior, uncertainty will be introduced in the collaboration trust evaluation. Uncertainty is also introduced by the fact that the edge devices often generate inaccurate and incomplete information. In the CEEC environment, any slight change may lead to mismatch between the calculation result of the trust value and the real-time state of the edge device, thus affecting the accuracy of the trust value. Therefore, in the continuous cycle, we use fuzzy logic to alleviate this mismatch. In this paper, the input-output relation of the fuzzy algorithm based on the IF-THEN rule is constructed. The fuzzy logic algorithm designed in this paper includes four trust factors generated by fuzzy sets as input, which are direct interaction success rate factor (DISRF), adjacent distance factor (ADF), public interaction success rate factor (PISRF) and cooperative incentive mechanism factor (CMF). Their membership functions are shown in Figure 3.  
1. Direct interaction success rate factor (DISRF) represents the interaction success rate between an edge device and its neighboring edge devices. It is an entity-centric trust factor. Due to the high dynamic of edge devices and the unreliability of wireless communication transmission, the number of neighboring edge devices is constantly changing, which leads to the uncertainty of their direct trust relationship. With the decrease in the success rate of direct interaction, the uncertainty of the direct trust relationship with neighboring edge devices will increase. The calculation of the interaction success rate between an edge device and its neighboring edge devices is shown in Equation (1). In this paper, DISRF is set to include two options: malicious and normal, ranging from 0 to 100%. The membership function of DISRF is shown in Figure 3a. 2. Adjacent distance factor (ADF) represents the interaction distance between the host and guest of an edge device group. It is an entity-centric trust factor. Because the density and environment of the edge device group will change over time, the recommendation weight of the edge device group is uncertain when evaluating the collaborative recommendation trust. At the same time, recommended trust is mainly obtained based on the indirect trust relationship between the public edge devices of the host and guest edge devices. As the adjacent distance between the host and guest edge devices increases, the uncertainty of its indirect trust relationship with the public edge devices will also increase. The calculation of the interaction distance between the host and guest of edge device group is shown in Equation (2). In this paper, ADF is set to include three options: short, medium and long, ranging from 0 to 300 m. The membership function of ADF is shown in Figure 3b. 3. Public interaction success rate factor (PISRF) represents the interaction success rate between the host and guest of an edge device group and their public edge devices. It is an entity-centric trust factor.