## 5 Fuzzy Logic Approaches in Trust Management
### Effective Metrics and Comparative Analysis of Methods Based on These Metrics
#### 5.2.2 Simulation Environment  
Simulation and evaluation software stand as indispensable tools in the articles under review, enabling researchers to model, analyze, and validate their hypotheses, experiments, and findings. These tools serve as conduits for simulating intricate scenarios, testing pa rameters, and assessing the efficacy of proposed solutions within a controlled environment. Researchers typically provide detailed descriptions of the software employed, elucidating specific packages, algorithms, and models utilized in their studies. In the peer-reviewed articles surveyed in this study, sections dedicated to simulation applications and parameters are frequently included, offering insights into the methodologies employed, as illustrated succinctly in Figure 4. Such comprehensive utilization of simulation and evaluation software not only facilitates rigorous scientific inquiry, but also enhances the reproducibility and robustness of research outcomes in the field. In the following section, we discuss the results obtained from the simulation tools referenced in the articles, specifying the names of the simulation software utilized. In [28], Network Simulator 3 was used to simulate the implementation and testing of a trust-aware access control mechanism on constrained and non-constrained devices. The simulation results evaluate the trust model, which considers reputation, QoS, security aspects, and social relationships for computing trust values. Performance metrics such as response times, trust computation times, access control decisions, and overall system efficiency provide insights into a system's operational efficiency and effectiveness in managing trust and access control in IoT environments. A study [29] conducted using the Cooja simulator on the Contiki 3.0 operating system revealed significant findings. This research employed performance metrics such as the average number of parent changes, packet loss ratio, and end-to-end delay to evaluate the efficiency and reliability of the proposed FDTM-RPL protocol. The FDTM-RPL protocol proved effective in mitigating major attacks on IoT networks, including SYBIL, RANK, and BLACKHOLE attacks, thereby enhancing the network performance and security. In addition, when compared to standard protocols, the FDTM-RPL protocol exhibited substantial improvements in performance across various attack scenarios and network sizes. This enhanced performance is attributed to the protocol's trust-aware routing mechanism and fuzzy-based trust evaluation, both of which collectively contribute to its robustness against common IoT attacks. MATLAB software was used for the simulations in [33]. It used fuzzy c-means clustering to categorize users based on their trust values and behavioral parameters. Surface view diagrams were generated to visualize the trust models for both users and providers. Figure 4: Simulation environments [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Trust evaluation results are presented based on performance, elasticity, security, and user feedback to determine access permissions and resource allocation in cloud environments. MATLAB software was instrumental in conducting simulations of trust-based access control models using fuzzy logic. The study in [36] used the OMNET++ 5.6.2 simulation platform. The results showed a 19.11% improvement in the detection rate of malicious edge devices and a 16.20% reduction in the error detection rate. The Lightweight Fuzzy Collaborative Trust Evaluation Model (LFCTEM) also reduced error reporting, indicating its effectiveness in enhancing trustworthiness in CEEC networks. Comparative analysis with existing algorithms, such as the Dynamic Trust Evaluation Model (DTEM) and Fuzzy Lightweight Trust Evaluation and Evaluation Value (FLTEEV), confirmed the effectiveness of the scheme in detecting malicious behavior and improving trust assessments. The study [37] used Network Simulator Version 2 (NS2) to simulate 250 nodes in a 1000 m \(\times\) 1000 m network area.