## 5 Fuzzy Logic Approaches in Trust Management
#### 5.1.2 Cloud Network  
[PERSON] et al. proposed fuzzy logic to calculate the trust values of cloud users and service providers. The study proposes two trust-based access control models: user-based and cloud service providers-based. These models identify trusted resources for users and authorize access based on trust values. The models combine elasticity and performance evaluations to determine trust values, controlling access permissions for cloud resources. The paper also introduces subjective trust models based on user and service provider behavior, improving the accuracy and reliability of trust evaluations in cloud computing environments. The results demonstrate the effectiveness of the proposed trust-based access control models in enhancing security, resource allocation, and access control in cloud computing environments [33].  
The article uses an ontology-based approach and fuzzy logic to evaluate trust in cloud computing environments. It effectively captures and represents contextual information, enhancing the accuracy and reliability of trust assessments. The study aims to develop comprehensive trust-based access control models considering various factors and parameters to evaluate the trustworthiness of users and service providers in cloud computing settings [33].  
[PERSON] et al. presented a fuzzy-based trust evaluation framework for fog computing. Fuzzy logic is used to evaluate trust based on multiple parameters, such as performance, reliability, security, price, and reputation. The framework employs a fuzzy-based filtering algorithm to select fogs based on trust evaluation criteria such as availability, quality of service, security, user feedback, and cost. The article also highlights the importance of continuous improvement in trust evaluation mechanisms [34].  
[PERSON] et al. proposed a new trust management framework for multi-cloud environments by integrating fuzzy logic principles for trust calculation and a combination of subjective and objective trust evaluation. The model handles ambiguity in trust-related parameters and feedback data, allowing for a more flexible approach. The study's simulation validation confirms the effectiveness of the components, highlighting the importance of feedback evaluation and trust negotiation in improving trust values and security in multi-cloud environments [35].  
[PERSON] et al. proposed a fuzzy logic algorithm to evaluate the trust value of edge devices in a Cloud-edge-end Collaboration (CEEC) environment. The model includes four fuzzy trust factors, an incentive mechanism, trust weight allocation, and edge intelligence management. The fuzzy logic algorithm outputs the trust evaluation value (TEV), which is categorizedinto Untrust, Trust-3, Trust-2, and Trust-1. The incentive score mechanism ensures the quality of collaborative evaluations and recruits high-quality edge devices for cooperation. The model's ability to detect malicious behavior, reduce error rates, and incentivize positive participation contributes to the advancement of trust management mechanisms in complex edge computing environments [36].  
This study assesses trust in edge devices using the fuzzy logic algorithm in addition to a CEEC computing power architecture and the MATLAB Fuzzy Logic Library. This increases the assessment of trust, encourages collaboration, and strengthens security and dependability in networks for cloud-edge-end collaboration. Defuzzification procedures, fuzzy sets, and rules are used in the construction of the fuzzy logic algorithm [36].