## 5 Fuzzy Logic Approaches in Trust Management
#### 5.1.5 Mobile Network  
[PERSON] et al. introduced a fuzzy comprehensive evaluation method to quantify trust in dynamic trust management scenarios. The authors proposed a trust quantification algorithm that considers fuzziness and uncertainty in trust relationships, enhancing its robustness and applicability in dynamic and open network environments. The algorithm'seffectiveness is demonstrated through simulation results, indicating alignment with entity behavior. The fuzzy comprehensive evaluation method contributes to trust management by providing a systematic and effective approach to quantifying trust values, making it valuable for decision-making processes in various industries and organizations. The paper emphasizes the importance of considering fuzziness and uncertainty in trust quantification and its contribution to trust management practices [44]. The article discusses the use of credit and reputation mechanisms in trust quantification, which are integral components of the process. Credit mechanisms evaluate an entity's trustworthiness based on behavior information and interactions with other entities. They represent the evaluation result of the owner of an object on a subject after certain interactions. The reputation mechanism considers the degree of trust of an object on a subject based on multiple credit values over time. It is calculated by combining credit values from different interactions with appropriate weights. The article provides a comprehensive approach to trust quantification that considers both qualitative aspects captured by fuzzy logic and quantitative aspects represented by credit and reputation evaluations. This multi-faceted evaluation process enhances the robustness and accuracy of trust assessment in dynamic network environments [44]. [PERSON] et al. incorporate probabilistic linguistic term sets (PLTS) to improve trust scaling by providing a more accurate and flexible way to express trust evaluations. PLTS allows decision makers to depict evaluation information using multiple linguistic terms with corresponding probabilities, capturing uncertainty and hesitancy in trust assessments. This approach contrasts with hesitant fuzzy linguistic term sets (HFLTS), which may lead to inaccuracies in reflecting expert views. PLTS also distinguishes the opinions of different recommenders more clearly, avoiding suboptimal solutions. The fuzzy algorithm used in the model handles uncertainty and vagueness in decision-making processes, allowing decision makers to express their opinions using linguistic terms representing degrees of trust or trustworthiness. The fuzzy logic helps to quantify and process subjective information, resulting in more accurate trust evaluations. The model also adopts the Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) method for decision making, addresses context-dependent trust modeling, handles vagueness and uncertainty, and constructs a structural trust evaluation by decomposing trust into multiple building blocks. This comprehensive approach to trust modeling addresses the complexities and challenges associated with trust assessment in various application scenarios [45]. This study, in addition to the fuzzy logic algorithm, uses the Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) method to evaluate trust levels. This robust decision-making technique combines three subordinate orders to rank the alternatives. The integration of the MULTIMOORA method with the fuzzy logic algorithm provides a comprehensive and structured approach for trust evaluation. The fuzzy logic algorithm handles uncertainty and vagueness, whereas the MULTIMOORA method fuses multiple criteria to derive a final ranking based on the trustworthiness of the alternatives. This research aims to create a robust trust evaluation model that considers various trust-related characteristics, leading to more accurate and context-aware assessments in multi-agent systems and networked environments [45]. [PERSON] et al.