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A Feedback-Driven Lightweight Reputation Scheme for IoV

机译:iov的反馈驱动的轻量级声望方案

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摘要

Most applications of Internet of Vehicles (IoVs) rely on collaborations between nodes. It is therefore of vital importance in building up the implicit trust between these nodes, the false information flow in-between these nodes poses the challenging trust issue in rapidly moving IoV nodes. To resolve this issue, a number of mechanisms have been proposed in the literature for the detection of false information and establishment of trust in IoVs, most of which employ reputation scores as one of the important factors. However, it is critical to have a robust and consistent scheme that is suitable to aggregate a reputation score for each node based on the accuracy of the shared information. Such a mechanism has therefore been proposed in this paper. The proposed system utilises the results of any false message detection method to generate and share feedback in the network, this feedback is then collected and filtered to remove potentially malicious feedback. A dynamic reputation score system is produced for each node. The reputation system has been experimentally validated and proved to have high accuracy in the detection of malicious nodes sending false information and is robust or negligibly affected in the presence of spurious feedback.
机译:车辆互联网(IOV)的大多数应用依赖节点之间的合作。因此,在构建这些节点之间的隐式信任方面,这些节点之间的虚假信息流在快速移动的IOV节点中的虚拟信息流程中的虚假信息流程是至关重要的。为了解决这个问题,在文献中提出了许多机制,以检测虚假信息和在IOV中的信任建立,其中大部分是雇佣得分作为重要因素之一。然而,具有坚固且一致的方案至关重要,该方案适于基于共享信息的准确性聚合每个节点的信誉分数。因此,本文提出了这种机制。所提出的系统利用任何错误消息检测方法的结果来在网络中生成和共享反馈,然后收集并过滤以删除可能的恶意反馈。为每个节点生成动态信誉分数系统。声誉系统已经通过实验验证,并证明在检测到发送虚假信息的恶意节点中具有高精度,并且在存在虚假反馈的情况下具有稳健或可忽视的影响。

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