A Beamforming Signal-Based Verification Scheme for Data Sharing in 5G Vehicular Networks

Van Linh Nguyen*, Po Ching Lin, Ren Hung Hwang


研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)


Vehicle-to-Everything (V2X) communications are vital for autonomous vehicles to share sensing data about the surrounding environment, particularly in non-line-of-sight (NLOS) areas where the camera and radar systems often perform poorly. However, an insider adversary such as a compromised vehicle can disseminate false sensing data that even a signature scheme cannot counter. Trusting such shared data, the surrounding vehicles may be trapped to react unexpectedly and potentially poses the risk of a fatal crash. In this work, we introduce a prospective cooperative verification scheme to support both the host vehicles and V2X edge applications in validating the truthfulness of sharing data in the fifth-generation (5G) vehicular networks. First, the detection systems at the host vehicle (local detector) and the road-side unit (RSU) (global detector) separately recreate a trajectory of a target vehicle by extracting its status and attributes from the received Cooperative Awareness Messages (CAM). Simultaneously, they also build another trajectory of the vehicle by independently fusing real-time measurement metrics from signal-based positioning. We then perform a Student's t-test to detect any significant differences between the extracted trajectory and the corresponding measured one. Finally, the quantified evidence from the local and global detector tests will be fused through the Dempster-Shafer fusion for the final decision, i.e., whether the target vehicle is trustful. Besides the theoretical analysis of basic limits, we perform extensive evaluations of the work in cases of both sparse and heavy traffic densities. Through the simulation, this work demonstrates its significant effect in terms of detection performance and response time, particularly for detecting Sybil and false data attacks quickly.

頁(從 - 到)211723-211737
期刊IEEE Access
出版狀態Published - 2020


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