车辆互联网中的干扰器定位:场景、实验和评估

Ahmed Mohamed Hussain, Nada Abughanam, Savio Sciancalepore, E. Yaacoub, Amr S. Mohamed
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摘要

车联网(IoV)模式旨在通过向网络中的联网车辆传输预警和信息娱乐信号,提高道路安全性,并为联网车辆提供舒适的驾驶体验。车联网的独特特性,如移动性和无处不在的互联网连接,使其网络面临许多网络攻击。特别是,干扰攻击对其性能构成了相当大的风险,因为它们可以显著影响车辆的功能,可能导致密集网络中的碰撞。本文提出了一种新的方案,能够检测和定位在车联网中进行的干扰攻击。我们考虑了几种情况,例如,联网车辆和干扰器静态定位,如停在街道上时,以相同的方向和不同的速度移动,以及相反的方向移动。我们利用接收信号的物理层特性,特别是接收信号强度(RSS),并设计了一种基于部署在车辆上的一组天线的解决方案,以最大限度地减少干扰器定位误差。具体来说,我们计算了干扰器发射和全向天线阵列接收的功率,并使用这些值来估计前面提到的场景中干扰器的位置。通过广泛的模拟活动,我们对我们的算法进行了深入的研究,评估了几个系统和通道参数对测量误差的影响。在所有场景下获得的结果都显示出显著的定位精度,即根据信道条件,定位精度在0.23米到13米之间。
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Jammer Localization in the Internet of Vehicles: Scenarios, Experiments, and Evaluation
The Internet of Vehicles (IoV) paradigm aims to improve road safety and provide a comfortable driving experience for Internet-connected vehicles, by transmitting early warning and infotainment signals to Internet-connected vehicles in the network. The unique characteristics of the IoV, such as their mobility and pervasive Internet connectivity, expose such networks to many cyberattacks. In particular, jamming attacks represent a considerable risk to their performance, as they can significantly affect vehicles’ functionality, possibly leading to collisions in dense networks. This paper presents a new scheme enabling the detection and localization of jamming attacks carried out within an IoV network. We consider several scenarios, e.g., where the Internet-connected vehicles and the jammer are statically positioned, as when parked on a street, moving in the same direction and with variable speeds, and moving in opposite directions. We leverage the physical-layer characteristics of the received signals, particularly the Received Signal Strength (RSS), and devise a solution minimizing the jammer localization error based on a set of antennas deployed on the vehicle. Specifically, we compute the power emitted by the jammer and received by the arrays of omnidirectional antennas and we use such values to estimate the location of the jammer in the previous-cited scenarios. Through an extensive simulation campaign, we provide a thorough study of our algorithm, evaluating the effect of several system and channel parameters on the measurement error. The results obtained for all scenarios show a significant localization accuracy, i.e., ranging from 0.23 meters to 13 meters, depending on the channel conditions.
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