## 5 Fuzzy Logic Approaches in Trust Management
### Effective Metrics and Comparative Analysis of Methods Based on These Metrics
#### 5.2.1 Comparative Analysis Based on Metrics
* Dynamicity: The scheme's utilization of real-time past experience, credit-based calculations, and peer recommendations for trust evaluation reflects a dynamic approach to trust management, allowing for adaptation to changing network conditions and the dynamic behavior of sensor nodes. * Context awareness: The hierarchical trust evaluation approach and the use of fuzzy logic in the scheme demonstrate a level of context awareness by considering the feedback from cluster heads of different clusters and the past reputation of cluster heads given by others. This context-aware approach contributes to a more informed trust assessment. The study in [41] addressed several important criteria in trust evaluation, including integrity and dynamicity. * Integrity: This method focuses on detecting dishonest recommendation attacks and ensuring the integrity of trust values exchanged between nodes. Using the FTM and ABC algorithms, the approach aims to maintain the integrity of trust evaluations and identify malicious nodes that may compromise network integrity. * Dynamicity: This methodology accounts for the dynamic nature of sensor networks by considering factors such as node mobility, environmental changes, and node behavior variations. The study in [33] covered a number of significant factors, such as context awareness, scalability, availability, integrity, privacy, and dynamicity:  
* Scalability: This is defined as the ability of a cloud system to function well when changes occur in the volume or size to satisfy user needs. By incorporating scalability as a factor in trust evaluation, the models aim to ensure that cloud resources can be scaled effectively to accommodate varying workloads. * Integrity: By evaluating factors such as security, performance, and user feedback, the models contribute to maintaining the integrity of cloud resources and access control mechanisms. * Availability: In the context of cloud computing, users can access resources in the correct format and location. By assessing availability as a parameter, the models aim to ensure that cloud services are accessible and reliable for users. * Dynamicity: By incorporating dynamic trust evaluation mechanisms, the models adapt to changing conditions and user interactions, thereby enhancing the responsiveness and adaptability of access control in cloud environments. The study in [35] satisfied the trust evaluation criteria of scalability, integrity, availability, and dynamicity:  
* Scalability: This model is designed to handle large-scale computing problems in multi-cloud environments, making it scalable. * Integrity: The inclusion of a feedback evaluation component in the framework helps identify and rectify fake feedback, enhancing the integrity of trust values. * Availability: The trust negotiation component of the framework facilitates the generation of service level agreements (SLAs) based on trust values, thereby enhancing the availability of cloud computing services. * Dynamicity: This model incorporates fuzzy logic principles to handle the dynamic and uncertain nature of trust-related parameters and feedback data in multi-cloud environments, making it dynamic. Several crucial factors in assessing trust were covered in the article [36], including context awareness, scalability, availability, integrity, and dynamicity:  
* Scalability: By utilizing fuzzy logic and incentive mechanisms, the model can be scaled to accommodate varying numbers of edge devices and adapt to changes in the network environment, making it suitable for scalable trust evaluation in CEEC networks. * Integrity: By incorporating an incentive mechanism and a negative decay mechanism, the model promotes honest and cooperative behavior among edge devices, thereby contributing to the integrity of trust evaluations in the CEEC environment.