PDTM: A Provenance-Driven dynamic Trust Management model for IoVs

Abstract

Internet of Vehicles (IoVs) play an essential role in traffic safety and travel efficiency. However, there is still risk where malicious vehicles without validation or evaluation, to tamper with messages and pollute network with compromised information. Upon fruitful computing resource in 6G, this paper proposes a Provenance-Driven dynamic Trust Management (PDTM) model for IoVs. PDTM integrates data provenance into the trust evaluation of vehicles and messages. Different from literature that only evaluates trust through vehicle, the high reliability of Road Side Unit (RSU) is concerned herein. Specifically, the recommendation trust is calculated by RSU, to eliminate false recommendations of malicious vehicles. Considering the dynamic and real-time nature of trust, this paper proposes a global-based threshold update mechanism. The mechanism can dynamically adjust the threshold, according to the trust value of vehicles. Simulation experiments verify the validity of PDTM in the detection rate of malicious vehicles and delivery rate of authentic messages.