F-RouND: Fog-based Rogue Nodes Detection in Vehicular Ad hoc Networks

Anirudh Paranjothi, Mohammed Atiquzzaman, Mohammad S. Khan
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引用次数: 3

Abstract

Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers used either cryptography, trust values, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose fog-based rogue nodes detection (F-RouND), a fog computing scheme, which dynamically creates a fog utilizing the on-board units (OBUs) of all vehicles in the region for rogue nodes detection. The novelty of F-RouND lies in providing low processing delays and FPR at high vehicle densities. The performance of our F-RouND framework was carried out with simulations using OMNET ++ and SUMO simulators. Results show that F-RouND ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at high vehicle densities compared to existing rogue nodes detection schemes.
F-RouND:基于雾的车辆自组织网络流氓节点检测
车辆特设网络(VANETs)使车辆可以广播信标信息,以确保道路安全。VANETs中的流氓节点广播恶意信息,导致潜在危险,包括车辆碰撞。以前的研究人员使用加密技术、信任值或过去的车辆数据来检测恶意节点,但它们受到高处理延迟、开销和假阳性率(FPR)的影响。我们提出了一种基于雾的流氓节点检测(F-RouND)的雾计算方案,该方案利用区域内所有车辆的车载单元(OBUs)动态创建一个雾来进行流氓节点检测。F-RouND的新颖之处在于在高车辆密度下提供低处理延迟和FPR。利用omnet++和SUMO模拟器对F-RouND框架的性能进行了仿真。结果表明,与现有的流氓节点检测方案相比,F-RouND在高车辆密度下确保处理延迟降低45%,开销降低12%,FPR降低36%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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