## 5 Fuzzy Logic Approaches in Trust Management
### Effective Metrics and Comparative Analysis of Methods Based on These Metrics  
In this section, we present an exhaustive analysis of trust level evaluation methodologies, with a specific emphasis on the fuzzy algorithm. Our review centers on key metrics including scalability, privacy, dynamics, integrity, availability, and context awareness, as delineated in Table 2. We aim to elucidate the efficacy and applicability of fuzzy algorithms in addressing these critical facets of trust assessment within diverse network environments. Initially, we provide brief definitions of these criteria, followed by a discussion of how each criterion is addressed in the reviewed literature. **Scalability** is paramount in evaluating trust within 6G networks. Given the burgeoning demands for data rates, low latency, and reliability, scalability becomes indispensable. As 6G networks are envisioned to accommodate a vast array of connected devices and handle high data rates, an adaptable network infrastructure is imperative [87]. Furthermore, the scalability enhancements expected in 6G vis-a-vis current technologies underscore the need for scalable solutions capable of accommodating the anticipated surge in connected devices. **Integrity** assumes a pivotal role in trust evaluation within 6G networks, ensuring their reliability and security. It encompasses the trustworthiness of the system, data, and entities involved, pivotal for upholding the authenticity and consistency of information within 6G networks [88]. Maintaining network integrity is fundamental for fostering trust among connected devices and facilitating the secure exchange of information, and is pivotal for the success of 6G networks. \begin{table} \begin{tabular}{c c c c c c c c c c} \hline \hline
**Category** & **Type** & **Technology** & **Ref.** & **Scalability** & **Integrity** & **Availability** & **Privacy** & **Dynamicity** & **Context Awareness** \\ \hline \multirow{4}{*}{NoT Networks} & Alof 1 & FL and Neural & \multirow{4}{*}{[30]} & \multirow{2}{*}{yes} & \multirow{2}{*}{yes} & \multirow{2}{*}{yes} & \multirow{2}{*}{yes} & \multirow{2}{*}{yes} & \multirow{2}{*}{-} & \multirow{2}{*}{yes} & \multirow{2}{*}{yes} \\  & & Network & & & & & & & & \\ \cline{2-8}  & IoT 7 & FL and TFM & [44] & yes & yes & yes & - & yes & Some degree \\ \cline{2-8}  & IoT & FL & [29] & yes & yes & yes & - & yes & yes \\ \cline{2-8}  & IoT 6 & FL & [31] & - & yes & yes & - & yes & - \\ \cline{2-8}  & IoT & FL & [32] & yes & yes & yes & - & yes & yes \\ \hline \multirow{4}{*}{Ad-Hoc Networks} & MANETs 9 & FL and Security & [37] & - & yes & - & - & yes & - \\ \cline{2-8}  & FANETs 5 & FL and ontology & [38] & definitely yes & yes & - & Some degree & yes & - \\ \cline{2-8}  & FANETs & FL and ML and BC & [39] & yes & yes & - & - & yes & yes \\ \cline{2-8}  & VANETs 6 & FL and Subjective & [40] & yes & yes & yes & yes & yes & - \\ \hline \multirow{4}{*}{Mobile Networks} & SDN & FL and BC & [46] & yes & yes & yes & yes & - & - \\ \cline{2-8}  & SC & FL & [47] & - & yes & yes & yes & yes & - \\ \cline{2-8}  & SC & FL and TFM & [28] & - & yes & - & yes & - \\ \cline{2-8}  & 6 GWN 12 & FL and GAN & [49] & - & yes & yes & - & yes & yes \\ \cline{2-8}  & SC & FL and TLA & [48] & yes & yes & yes & yes & yes & yes \\ \cline{2-8}  & SC & FL and Multimoora & [45] & yes & yes & - & - & yes & yes \\ \cline{2-8}  & IWSN 8 & FL & [43] & - & yes & yes & - & yes & yes \\ \cline{2-8}  & WSN 11 & FL and TRM & [42] & - & yes & - & - & yes & Some degree \\ \cline{2-8}  & WSN & FL and ABC & [41] & - & yes & - & - & yes & - \\ \hline \multirow{4}{*}{Cloud Networks} & CN 3 & FL and ontology & [33] & yes & yes & yes & - & yes & - \\ \cline{2-8}  & CN & FL and Subjective & [35] & yes & yes & yes & - & yes & yes \\ \cline{2-8}  & CEC 2 & FL & [36] & yes & yes & yes & - & yes & yes \\ \cline{2-8}  & FCN 4 & FL and broker-based & [34] & - & yes & yes & yes & yes & - \\ \hline \hline \end{tabular}