Blockchain-Driven Incentive Mechanism and Multi-Level Federated Learning Method for Behavior Detection in the Internet of Vehicles
Quan Shi, Lankai Wang, Yinxin Bao, Chen Chen · Symmetry · 2025
With the rapid advancement of intelligent transportation systems (ITSs), behavior detection within the Internet of Vehicles (IoVs) has become increasingly critical for maintaining system security and operational stability. However, existing detection approaches face significant challenges related to data privacy, node trustworthiness, and system transparency. To address these limitations, this study proposes a blockchain-driven federated learning framework for anomaly detection in IoV environments. A reputation evaluation mechanism is introduced to quantitatively assess the credibility and contribution of connected and autonomous vehicles (CAVs), thereby enabling more effective node management and incentive regulation. In addition, a multi-level model aggregation strategy based on dynamic vehicle selection is developed to integrate local models efficiently, with the optimal global model securely recorded on the blockchain to ensure immutability and traceability. Furthermore, a reputation-based prepaid reward mechanism is designed to improve resource utilization, enhance participant loyalty, and strengthen overall system resilience. Experimental results confirm that the proposed framework achieves high anomaly detection accuracy and selects participating nodes with up to 99% reliability, thereby validating its effectiveness and practicality for deployment in real-world IoV scenarios.