Evaluating Granularity in Markov Chain-Based Trust Models for Vehicular Ad Hoc Networks (VANETs)

Abstract

Trust management is a critical research pillar in Vehicular Ad Hoc Networks (VANETs), where the reliability of shared data depends entirely on driver integrity. In these networks, a driver's reputation is dynamically constructed based on the veracity of their recent message history: consistent reliability builds trust, while frequent misinformation leads to exclusion. This study analyses driver announcement characteristics by modelling behavioural transitions—specifically the frequency and nature of shifts between "good" and "bad" states. To facilitate this analysis, three distinct Markov chain-based behavioural models are evaluated with varying degrees of granularity: a 4-state model, a 7-state model, and a high-resolution 11-state model. By simulating announcement and reporting patterns, each model's ability to reflect nuanced behavioural shifts is assessed. Our results confirm that increasing the number of trust states significantly enhances the system's ability to capture complex, dynamic driver behaviours, providing a more robust framework for security in VANETs.