## 5 Fuzzy Logic Approaches in Trust Management
#### 5.1.1 IoT  
[PERSON] et al. proposed fuzzy logic algorithms to infer trust values for IoT devices based on multiple dimensions. The system consists of a fuzzy control system (FCS) that analyzes analog input values and generates trust values. This model enhances security by considering security evidence inferred from transactions. The system uses fuzzy logic to handle the vagueness and uncertainty of information in IoT environments and includes a lightweight and flexible access control mechanism based on Distributed Capability-Based Access Control (DCapBAC). The system's performance is evaluated in terms of trust expectations, reward requests, fuzzy trust quantification, authentication, token validation, and full trust processing [28]. This study uses a multidimensional trust model to assess trust in IoT environments, including fuzzy logic algorithms and other technologies. It considers reputation systems, which assess the reliability and historical behavior of IoT devices, and security mechanisms, which quantify the trust values. These factors help to evaluate the security posture of IoT devices and ensure secure interactions within the ecosystem. The multidimensional trust model provides a holistic assessment of trust values for IoT devices by considering factors such as reputation, QoS, security, and social relationships [28]. [PERSON] et al. proposed the multi-fuzzy, dynamic, and hierarchical trust model (FDM-IoT) as a dynamic, comprehensive, and hierarchical trust model designed to enhance routing security in IoT networks. It calculates trustworthiness in multiple dimensions, including quality of service, quality of peer-to-peer communication, and contextual information. The model is designed to be flexible and adaptable to changing environmental conditions. Fuzzy logic is used to handle uncertainty, making trust decisions more robust. The model is integrated into the Routing Protocol for Low power and Lossy Networks (RPL) protocol, enhancing security and network performance. Performance evaluations show the model's effectiveness in enhancing routing security in dynamic IoT environments [29]. This study utilizes a multistep fuzzy model to evaluate the level of trust among IoT devices. [PERSON] et al. introduce a fuzzy attribute trust algorithm as a supplementary approach to address trust evaluation challenges in Artificial Intelligence of Things (AIoT) networks. The fuzzy algorithm addresses the lack of interaction records and uncertainty in AIoT environments, allowing for a more accurate evaluation of smart terminals' trust values. It also mitigates mismatches between trust values and the real-time state of smart terminals. The fuzzy algorithm is scalable and adaptable, allowing for the addition of new fuzzy factors to accommodate different application scenarios. The model's performance is competitive with existing approaches, demonstrating its effectiveness in addressing trust evaluation challenges in AIoT networks. The model's performance, adaptability, and scalability contribute to its potential for enhancing the trustworthiness of smart terminals in AIoT environments [30]. In addition to the fuzzy logic algorithm, this study utilized a neural network-based trust evaluation algorithm to assess the level of trust in smart terminals within AIoT networks. A neural network, consisting of four layers, was deployed at the terminal layer of the cloud-edge-terminal collaborative AIoT trust model (CET-AoTM). It complements the fuzzy logic algorithm by evaluating trust based on historical interactions and experience attributes. This approach enhances the CET-AoTM model's trust evaluation capabilities, addressing challenges in dynamic AIoT environments and the lack of historical interaction records for new smart terminals. The neural network algorithm contributes to the overall effectiveness and accuracy of the trust evaluation, providing a comprehensive assessment of smart terminals in AIoT networks [30]. [PERSON] et al.