A UAV-Assisted Traceable and Hierarchical Trust Management in VANET for Disaster Data Collection

In disaster scenarios, secure and reliable data collection in Vehicular Ad Hoc Network (VANET) is crucial, yet the network often suffers from issues such as infrastructure damage, network partitioning, and vulnerabilities to attacks (e.g., False Data Injection and Black Hole Attack). Trust management is a promising solution to prevent these attacks. However, in infrastructure-less and partitioned disaster areas, existing trust schemes face problems of trust evidence sparsity and evaluation inconsistency, leading to inaccurate detection. To address these limitations, we propose an Unmanned Aerial Vehicle (UAV)-Assisted Traceable and Hierarchical Trust Management scheme (UATHTM). The UATHTM includes a Vehicle-to-Vehicle (V2V) local trust model and a UAV-to-Vehicle (U2V) global trust model. The former facilitates rapid detection of false data, while the latter is designed for accurately tracing malicious vehicles. Specifically, the V2V model constructs a mutual adjustment between entity-centric and data-centric assessments, continuously refining trust for accurate local detection. The U2V model incorporates a new trust metric based on disaster trajectory similarity to enhance the accuracy of global tracing, through leveraging the comprehensive view of UAVs. Extensive simulations demonstrate that the UATHTM scheme outperforms existing trust management schemes, showing higher precision, recall, and F1-score in detecting false data and malicious vehicles in VANET under challenging conditions.