利用支持 SDVN 的交通灯合作框架进行 SIoV 移动性管理

Neetesh Kumar, Navjot Singh, Anuj Sachan, Rashmi Chaudhry
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摘要

社会车辆互联网(SIoV)是一种新兴的互联车辆网络框架,专门用于社会车辆之间共享和传播交通更新、天气状况、停车位等重要信息。本研究的目的是在紧急车辆之间形成一个 SIoV 网络,使其频繁通信,从而提高城市中车辆通过十字路口时的吞吐量、平均等待时间、队列长度和速度。为此,我们提出了一种新颖的智能交通灯控制器辅助软件定义车辆网络的应急车辆 SIoV 框架。应急车辆利用软件定义车载网络(SDVN)架构在车辆与车辆、车辆与基础设施之间进行通信,从而形成一个 SIoV 网络。SDVN 模块用于提供两种基本服务:1) 基于 SIoV 的道路车道优先化,以及 2) 为智能交通灯控制器生成拥堵预防信号。我们提出了一种 SDVN-MP 算法,通过 SDVN 控制器反馈信号生成有效的交通灯控制信号。此外,为了改善 SIoV 在城市中的通行情况,提出了两个优先级:1)SIoV;2)有 SIoV 的道路车道。第一级优先级是为社会车辆实体分配更高的权重,第二级优先级是根据 SIoV 数量确定相应车道的优先级。利用城市流动性模拟器对印度城市 OpenStreetMap 进行了实际模拟研究,验证了所提出的框架。实验结果表明,在平均等待时间、平均速度、平均队列长度和平均吞吐量指标上,SDVN-MP 模型分别提高了 22.5% 至 55.2%、1.2% 至 82.7%、1.6% 至 38.4% 和 1.8% 至 12.4% 的(最先进)比较性能。
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SIoV Mobility Management using SDVN-enabled Traffic Light Cooperative Framework
Social Internet of Vehicles (SIoV) is an emerging connected vehicular networking framework among specialized social vehicles to share and disseminate important information like traffic updates, weather conditions, parking slots, etc. This study aims to form an SIoV network among emergency vehicles for their frequent communication to improve the throughput, average waiting time, queue length, and speed during vehicular movement while crossing the intersection in the city. To address this, we propose a novel smart traffic light controller-assisted software-defined vehicular networking-enabled SIoV framework for emergency vehicles. Emergency vehicles form an SIoV network by utilizing Software-Defined Vehicular Networking (SDVN) architecture in Vehicle to Vehicle and Vehicle to Infrastructure communication. The SDVN module is used to offer two essential services: 1) SIoV-based road-lane prioritization, and 2) congestion prevention signal generation for the smart traffic light controller. An SDVN-MP algorithm is proposed to generate an effective traffic light control signal with an SDVN controller feedback signal. Furthermore, to improve the SIoV movement in the city, two levels of prioritization: 1) SIoV, and 2) the road lane with SIoV, are done. The first level of prioritization is to assign higher weightage to the social vehicular entities, and the second level is to prioritize the respective road lane based on SIoV quantity. The proposed framework is validated through a realistic simulation study on the Indian city OpenStreetMap utilizing the Simulation of Urban MObility simulator. The experimental findings demonstrate that the SDVN-MP model enhances (state-of-the-art) comparative performance by 22.5% to 55.2%, 1.2% to 82.7%, 1.6% to 38.4%, and 1.8% to 12.4% for average waiting time, average speed, average queue length, and average throughput metrics, respectively.
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