BTSR: Blockchain-Based Trust Management Scheme for Social Routing in IoVs

With the rapid development of Internet of Vehicles (IoVs), the imperative for data security within IoVs has increased, especially regarding data tampering and spreading false data. To address this issue, a secure blockchain-based trust management is employed to detect malicious behaviors. For instance, legitimate vehicles may be exploited to disseminate incorrect data, while blockchain technology maintains the transparency and reliability of trust value. However, existing schemes are challenged by the slow generation of trust opinions (lack of trust opinions due to insufficient interactions) and high latency. To solve such problems, we propose a Blockchain-based Trust Management Scheme for Social Routing in IoVs (BTSR). Specifically, the trust management for vehicles we proposed then considers four types of trust: direct trust, indirect trust, global trust, and roadside unit (RSU) trust. The direct trust is evaluated by the data-sending vehicle, indirect trust is provided by other vehicles, global trust originates from RSUs, and RSU trust is used to evaluate RSUs by Trusted Authority (TA). Furthermore, to speed up the generation of trust opinions, a verifiable active detection mechanism is employed via the RSU to increase the frequency of interactions. Here, the sender transmits detection data to the receiver, which then forward it to the RSU for validation, thereby enhancing the interaction between the vehicle and the RSU. Meanwhile, to prevent active detection from being exploited by malicious RSUs and vehicles, the Trust Outlier Factor (TOF) algorithm is introduced to identify outlier RSUs whose trust opinions significantly deviate from those of other RSUs. Finally, we employ a dual-layer Hyperledger Fabric (HF) to reduce latency by lowering the consensus requirement at the edge layer, and to ensure data security by combining trust management at the core layer. A multitude of experiments confirms that our scheme surpasses baseline schemes, showcasing high precision, recall, and F -measure.