## 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  
To provide a comprehensive comparison, we analyzed the different methods using the aforementioned metrics. Each method has strengths and weaknesses that can be highlighted through comparative analysis. In [30], the authors demonstrated several aspects that align with the criteria of scalability, integrity, availability, dynamicity, and context awareness in trust evaluation:  
* **Scalability**: The CET-AoTM model exhibited scalability in trust evaluation by leveraging a cloud-edge-terminal collaborative architecture. This architecture allows for the distribution of trust evaluation tasks among different layers, enabling the system to scale efficiently as the number of smart terminals and interactions increases. * **Integrity**: The trust evaluation algorithm in the CET-AoTM model focuses on maintaining the integrity of trust values by considering factors such as historical interactions, cumulative experience attributes, and fuzzy-logic-based trust attributes. This approach helps to ensure the reliability and integrity of trust evaluations for smart terminals in AIoT networks. * **Availability**: The demand-driven cloud-edge-terminal collaboration mechanism in the CET-AoTM model enhances availability by adapting a trust evaluation based on specific computing service requirements. This mechanism ensures that trust evaluations are available when required for different types of tasks, balancing availability with the demands of AIoT services. * **Dynamicity**: The CET-AoTM model acknowledges the dynamic nature of AIoT environments by utilizing a fuzzy attribute trust algorithm to address uncertainty and adapt to changing conditions. This dynamic approach allows the trust evaluation system to adjust to the evolving trust requirements and environmental dynamics in AIoT networks. * Context Awareness: The demand-driven collaboration mechanism in the CET-AoTM model demonstrates context awareness in trust evaluation by considering the specific requirements of computing tasks in the AIoT. By adapting trust evaluation based on task demands, the model exhibits awareness of the context in which trust assessments are made, enhancing the relevance and effectiveness of trust evaluations. The study [44] addressed the criteria of scalability, integrity, and dynamicity in trust evaluation:  
* Scalability: Although the scalability of the proposed method is not explicitly discussed in the provided excerpts, the use of a fuzzy comprehensive evaluation method suggests a systematic approach that could potentially be scalable to larger networks or systems. * Integrity: By considering credit and reputation in trust quantification, this approach appears to address the integrity of trust assessments by incorporating past interactions and evaluations. * Dynamicity: The authors discuss the dynamism of trust, credit, and reputation, indicating that the proposed method accounts for changes in trust relationships over time, aligning with the dynamic nature of trust management. The article [29] addressed several important criteria related to the security and efficiency of routing in IoT networks. Here, we analyze the alignment of the proposed FDTM-IoT model with the criteria of scalability, integrity, availability, privacy, dynamicity, and context awareness. * Scalability: The FDTM-IoT model demonstrates scalability by applying it to various network sizes, from small- to large-scale IoT environments. The dynamic and hierarchical structure of the model allows for the addition or removal of dimensions and sub-dimensions, making it adaptable to different network scales. * Integrity: The trust calculations and fuzzy logic used in the FDTM-IoT model contribute to maintaining data integrity within the IoT network. By considering multiple dimensions, such as quality of service and contextual information, the model ensures that trust evaluations are comprehensive and reliable, enhancing overall data integrity.