## 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
* Context Awareness: The trust evaluation process considers various contextual factors, such as energy consumption, distance, delay, QoS requirements, and the trustworthiness of nodes. By incorporating these contextual parameters into the evaluation criteria, the system demonstrates the level of context awareness in trust evaluation. In [37], the authors addressed several important aspects, including scalability, integrity, and dynamicity:
* Scalability: This study proposes a secure trust-based multipath routing system that can potentially scale large MANETs by incorporating optimal fuzzy logic for route selection. However, specific scalability aspects, such as the ability to handle a large number of nodes or network size, are not explicitly discussed in the provided excerpts. * Integrity: The use of secure trust mechanisms and fuzzy logic for route selection indicates a focus on maintaining the data integrity within the network. By evaluating trust values based on various criteria, the system aims to ensure the integrity of the data transmission and routing decisions. * Dynamicity: The dynamic nature of MANETs, characterized by node mobility and changing network conditions, was considered in this study. The use of fuzzy logic for trust evaluation and multipath routing suggests an adaptive approach to handling dynamic network environments. Several crucial requirements of scalability, integrity, privacy, and dynamicity were covered in the paper [38]:
* Scalability: The detection accuracy of the model increases with network size, indicating that the scheme can effectively handle larger FANETs while maintaining trust and security. * Integrity: The TBCS model focuses on evaluating the trustworthiness of nodes based on their behavior, which contributes to maintaining the integrity of the network. By segregating malicious nodes and selecting secure Cluster Heads, the model enhances the overall integrity of communication in FANETs. * Privacy: This article emphasizes the importance of trust in segregating noncooperative and malicious nodes, which indirectly contributes to enhancing privacy in communication within FANETs. By identifying and isolating malicious nodes, the TBCS model helps to protect the privacy of legitimate network participants. * Dynamicity: The scheme's performance is highlighted as better than that of existing models when dealing with high-speed nodes and an increasing number of malicious nodes, demonstrating its adaptability to dynamic network conditions. The paper in [39] included a number of crucial factors in assessing trust, such as context awareness, scalability, integrity, and dynamicity:* Scalability: This article discusses the scalability of the FUBA model by having each drone evaluate the trustworthiness of adjacent drones and relay this information to a ground control station. This approach efficiently restricts the dissemination of trust data and evenly distributes the computational load, thereby enabling the deployment of scalable FUBA. * Integrity: The FUBA model aims to differentiate between legitimate and malicious drone actions, enhancing the integrity of the network by effectively evaluating and understanding node behaviors in FANETs. * Dynamicity: The FUBA model leverages fuzzy logic to handle uncertain and dynamic data related to UAV behavior in FANETs. This approach allows for adaptability and responsiveness to evolving tactics used by malicious drones, indicating a level of dynamicity in the trust evaluation process. * Context awareness: The FUBA model incorporates contextual information, such as weather conditions, signal strength, and historical data, to make more accurate decisions about the legitimacy of a drone's presence. This demonstrates the level of context awareness in the trust evaluation process, enabling the model to adapt to diverse operating environments and scenarios.