## 5 Fuzzy Logic Approaches in Trust Management
#### 5.1.3 Ad Hoc Networks  
[PERSON] et al. proposed fuzzy logic for trust evaluation in Mobile Ad Hoc Networks (MANETs). The fuzzy algorithm considers multiple network performance and security criteria, generates IF-THEN rules based on expert knowledge, and uses membership functions to map input variables to linguistic variables representing trust levels. The study evaluates the fuzzy logic approach's effectiveness in enhancing trust-based decision making for secure multipath routing, aiming to improve reliability and security in MANETs. The fuzzy algorithm's performance is evaluated in terms of its ability to enhance QoS, security, and trustworthiness in data communication [37].  
This study uses a combination of fuzzy logic and secure trust mechanisms to assess trust in MANETs. Secure trust mechanisms such as cryptographic techniques, digital signatures, and secure communication protocols are used to establish trust relationships among nodes. To enhance security, encryption protocols, authentication mechanisms, and access control policies have been implemented. Machine learning techniques are also used to analyze trust-related data and patterns, thereby enhancing the accuracy and efficiency of trust evaluation processes in MANETs. The integration of these technologies and methods with the fuzzy logic algorithm aims to create a robust trust evaluation system for secure multipath routing in MANETs, thereby enhancing the reliability and effectiveness of trust assessment in dynamic wireless network environments [37].  
[PERSON] et al. proposed fuzzy classification and optimization techniques to evaluate the trustworthiness of nodes in Flying Ad Hoc Networks (FANETs). The Trust-Based Clustering Scheme (TBCS) model demonstrated improved trust evaluation, enhanced performance, and scalability. It outperformed existing models like Community of Interest dynamic Hierarchical Trust management (COI-HiTrust) and AFStrust (fuzzy logic rules prediction method), as well as a model proposed by [PERSON] et al. in terms of accuracy, performance, and adaptability in FANETs. The model also outperformed cluster-based models in terms of the packet drop ratio, outperforming the AFStrust and COI-HiTrust trust models. The results and improvements in the article demonstrate the effectiveness of the TBCS model in enhancing trust, security, and performance in FANETs [38].  
[PERSON] et al. proposed fuzzy logic to assess drone trustworthiness in complex environments. The Fuzzy-based Unmanned Aerial Vehicles behavior analytics for trust management in FANETs (FUBA) model improves trust management in FANETs, particularly under challenging conditions like bad weather and poor signal strength. It also shows a lower error ratio. The FUBA model's scalability and practicality are addressed [39].  
[PERSON] et al. proposed a fuzzy comprehensive strategy, mutual authentication, and incentive mechanisms to ensure reliable communication, prevent malicious access, and encourage credible behaviors. It addresses security and performance aspects, enhancing privacy protection and message integrity. The scheme also introduces an incentive mechanism to encourage credible behaviors and uses lazy updates and dynamic storage structures to support On Board Unit mobility [40].  
This study discusses the use of an entropy method in trustworthiness evaluation, which corrects the original weight by integrating subjective and objective factors. This approach overcomes subjectivity and ensures a more reasonable result in trust evaluation.  
Combining the entropy method with fuzzy logic enhances the accuracy and reliability of trustworthiness assessment in Vehicular Ad Hoc Networks, improving the overall trustworthiness assessment process [40].