The Internet of Vehicles (IoV) is a cornerstone of Intelligent Transportation Systems (ITS), enabling seamless communication between vehicles, roadside units, and infrastructure. However, the dynamic, distributed, and heterogeneous nature of IoV raises significant challenges in trust management, particularly in mitigating malicious activities and ensuring secure data exchange. This paper proposes a Context-Aware and Dynamic Trust Management Scheme (CAD-TMS) for the IoV, designed to adaptively assess trust levels by incorporating real-time contextual factors and node behaviors such as vehicle speed, proximity, cooperation level, and historical behavior. The proposed scheme dynamically updates trust scores to adapt to the ever-changing vehicular environment, ensuring robustness against malicious nodes and misbehavior. In addition, CAD-TMS employs a weighted scoring mechanism that assigns varying importance to contextual parameters, enabling precise trust evaluation across diverse traffic conditions. To further enhance reliability, the scheme integrates reputation sharing among neighboring nodes and a trust-based decision process where only nodes above a threshold can communicate. Trust thresholds are dynamically updated based on network conditions, enabling stable real-time decisions, while low-trust nodes are restricted to maintain secure and reliable communication. The proposed CAD-TMS is implemented and evaluated in a simulation-based environment using NS-3 and SUMO under a realistic urban traffic scenario. The experimental results indicate that CAD-TMS achieves improved performance over benchmarks, including a 1.57% increase in trust, a 1.37% gain in throughput, a 2.16% improvement in Packet Delivery Ratio (PDR), and a 4.10% increase in accuracy.
Malik et al. (2026) studied this question.