In the literature [23], the cloud server set rewards for data collectors who actively collected sparse and high-quality data in remote areas so as to increase the collection contribution and, thus, improve the overall quality of data collection. In the literature [24], an incentive mechanism was designed: the winner of the competition was given the basic reward, and those who finished early were given extra rewards. These rewards would entice edge servers to complete computational tasks initiated by their peers and encouraged them to complete them quickly. In the literature [25], vehicles were encouraged to intentionally maintain trusted data sharing through the self-executing nature of smart contracts in a blockchain. It used the contribution of the vehicle as an incentive and adjusted the proportion of the reward according to how trustworthy the data were. In the literature [26], a penalty factor was introduced into the trust value of the vehicle, so that the vehicle refusing to provide service can obtain a lower trust value, and if the vehicle provided a false location, its trust value would be greatly reduced. This discouraged dishonest vehicles and incentivized selfish vehicles to actively offer their services. In the literature [27], users were motivated by the development of an effective voting mechanism with built-in consensus and punishment functions in the blockchain. The nodes involved in the verification would be rewarded with part of the deposit, while the abusive users would be punished and blacklisted. In the literature [28], UAVs were used to collect partially verifiable data, and random sampling by trusted third parties was used for joint trust evaluation. Therefore, the uncertainty of verifiable collected data increased the likelihood of detecting fake data uploads by mobile edge users, thus achieving the purpose of motivating users to upload trusted data honestly. In the literature [29], when evaluating trust in data in federated learning, a trust reward and punishment method was proposed in order to achieve the incentive of consensus of trust. When a task was completed, a certain amount of credit would be added. However, when the trust value was large, the proportion of increased trust value would be reduced so that the trust value of nodes added later could quickly catch up. In order to realize the fairness among all nodes, the punishment degree of each node was different. The higher the trust value, the harsher the punishment was to ensure the fairness of rewards and punishments. However, all the above research works were short-term incentives for the edge devices, and the effect of incentives was limited, which may cause the intermittent selfish behavior of the edge devices. Overall, our study investigates the gaps in the above studies and makes the following contributions:  
* Firstly, we propose a Cloud-Edge-End Collaboration (CEEC) computing power architecture and design a lightweight fuzzy cooperative trust evaluation model (LFCTEM) for edge devices, which fuzzies the uncertainty factors in the CEEC network. By constructing four fuzzy trust factors, we evaluate the trust value of edge devices according to the success rate of direct interaction, adjacent distance, public interaction success rate and cooperative positivity of the edge device group. * Secondly, we introduce the incentive mechanism in the trust evaluation algorithm to encourage the edge devices through the cooperation reward scores, quality reward scores and efficiency reward scores. We also adopt the incentive negative decay mechanism to improve the continuity of active cooperation of edge devices so as to avoid their intermittent selfish behavior. * Finally, we present experiments carried out on the OMNET++5.6.2 simulation platform, and further comparison of our algorithm with DTEM and FLTEEV algorithms, thus proving the effectiveness and advantages of our model.