Fuzzy Logic-Based Bayesian Trust Routing (Flbt-R) For Trust-Based Secure Routing In Vehicular Ad Hoc Networks (Vanets)

Resumen

Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation systems, enabling efficient and secure vehicle-to-vehicle (V2V) communication. However, ensuring trust and security in routing remains a significant challenge due to the dynamic nature of VANETs and the presence of malicious nodes. This paper proposes Fuzzy Logic-Based Bayesian Trust Routing (FLBT-R), an innovative trust-based routing model that integrates fuzzy logic and Bayesian inference to enhance security and reliability in VANET communications. The proposed model evaluates node trustworthiness based on multiple parameters such as past behavior, packet forwarding rate, and neighbor recommendations. Fuzzy logic enables adaptive trust assessment, while Bayesian inference refines trust values over time, effectively reducing false positives and negatives. Simulation results demonstrate that FLBT-R achieves higher malicious node detection rates (MDR), lower false positive rates (FPR), reduced computational overhead, and optimized average hop count compared to traditional trust-based routing schemes. These improvements establish FLBT-R as a robust approach for mitigating security threats and ensuring reliable data transmission in VANET environments.