Evaluation of Trust‐Based Models for Enhancing Security and Efficiency in Vehicular Ad‐Hoc Networks

ABSTRACT Vehicular ad‐hoc networks (VANETs) play a critical role in designing intelligent transportation systems (ITS) to enable spontaneous communication among vehicles (V2V) and between vehicles and roadside units (V2I). The VANETs issue revolves around providing safe, effective communication within the evolving scenario when security and performance are compromised through malicious attacks, node misbehavior, and forgery of information. The review of this study is to evaluate various trust‐based models in VANETs, analyze their effectiveness in improving security and efficiency, identify challenges in real‐world implementations, and propose recommendations for enhancing trust management to ensure reliable and secure vehicular communication. Types of trust models include reputation‐based, behavior‐based, and hybrid models. Methodologies like machine learning and algorithms like trust propagation are used. Findings show that trust‐based models in VANETs improve security by detecting malicious nodes, enhancing communication efficiency, and ensuring reliable data exchange. They balance security and performance for optimal network operation. Future research in trust‐based models for VANETs could focus on integrating machine learning for dynamic trust evaluation, enhancing scalability, developing robust attack detection mechanisms, and improving real‐time decision‐making for secure vehicular communication.