## 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
* Availability: The FDTM-IoT model is integrated into the RPL routing protocol as FDTM-RPL aims to improve network availability by enhancing security mechanisms and performance. The model's resistance to attacks such as BLACK-HOLE, SYBIL, and RANK attacks contributes to maintaining network availability under challenging conditions. * Privacy: While this article primarily focuses on trust-based routing security, the FDTM-IoT model's consideration of contextual information and the quality of peer-to-peer communication can indirectly contribute to privacy protection within IoT networks. * Dynamicity: The FDTM-IoT model is designed to be dynamic, allowing for real-time monitoring of the behavior of IoT and continuous trust evaluation. The adaptability of the model to changing environmental conditions and behaviors enhances its dynamicity, making it suitable for dynamic IoT environments. * Context Awareness: The FDTM-IoT model incorporates contextual information as a key dimension of trust evaluation. By considering contextual factors such as the mobility of things, security capabilities, and device intelligence capacity, the model demonstrates context awareness in assessing trustworthiness among IoT entities. The research [31] addressed several important aspects of trust evaluation, including scalability, integrity, availability, privacy, dynamicity, and context awareness. * Scalability: While this article primarily focuses on trust evaluation within this specific context, it provides a foundation for scalability by addressing the trustworthiness of interconnected IoMT devices. However, the scalability of the mechanism to larger IoMT networks or different deployment scenarios requires further investigation. * Integrity: The trust evaluation process in the FTM-IoMT includes assessing the integrity of nodes as one of the trust attributes. By considering integrity as a key factor in trust assessment, the mechanism aims to ensure the integrity of communication and interaction within the IoMT network. * Availability: By evaluating trust attributes and identifying compromised or Sybil nodes, the mechanism contributes to maintaining the availability of trustworthy services in the IoMT environment. * Dynamicity: The fuzzy-based trust management mechanism in the FTM-IoMT utilizes fuzzy logic processing to dynamically assess the trustworthiness of nodes based on attributes such as receptivity and compatibility. This dynamic evaluation contributes to adapting to changes in the IoMT network and addressing potential threats. The study in [32] addressed several important aspects of trust evaluation, including scalability, integrity, availability, dynamicity, and context awareness:* Scalability: The use of fuzzy logic and the Harmony Search Algorithm (HHO) in the routing protocol allows for scalable trust evaluation by considering multiple factors such as energy, distance, delay, overhead, QoS, and trust. This approach enables the system to handle a large number of nodes and effectively adapt to changing network conditions. * Integrity: By incorporating trust as one of the evaluation criteria in the routing protocol, the article ensures that data integrity is maintained during the routing process. Trust evaluation helps identify trustworthy nodes for data transmission, thereby enhancing the overall integrity of the network. * Availability: The trust evaluation mechanism based on fuzzy logic and the HHO algorithm ensures the availability of reliable routes for data transmission in IoT networks. By selecting optimal routes that consider trust levels, the system enhances the availability of communication paths within the network. * Dynamicity: The use of fuzzy logic and optimization algorithms allows for a dynamic trust evaluation in response to changing network conditions and node behaviors. The system can adapt to dynamic environments and adjust the trust levels based on real-time data and feedback.