OptiFlow:利用改进的集群路由优化智能交通系统中的交通流

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of Vehicular Technology Pub Date : 2024-10-30 DOI:10.1109/OJVT.2024.3488084
Roopa Tirumalasetti;Sunil Kumar Singh
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引用次数: 0

摘要

智能交通系统(ITS)在很大程度上依赖于车载 Ad hoc 网络(VANET)来促进有效通信,尤其是车对物(V2X)通信。然而,目前的研究发现,VANET 在节点管理、安全性和路由选择方面存在挑战,因此需要定制解决方案来解决这些问题。本研究介绍了一种基于集群的创新路由策略,该策略采用了增强型蜻蜓群优化(ESSO)和混合数据多标准决策评估(EVAmix MCDM)方法,专门用于优化 V2X 通信中的路由。现有的元启发式方法通常面临收敛速度慢、过早收敛和局部最优稳定性等问题,与之不同的是,所提出的方法取得了令人瞩目的成果。值得注意的是,在第 300 次迭代中,它将吞吐量提高了 6278 kbps,将数据包交付率(PDR)提高了 95.77$/%$,并将端到端延迟减少了 1856ms,表现优于现有的集群路由方法。我们的研究结果表明,在克服 V2X 通信的现有挑战方面,我们取得了实质性的飞跃。这一创新解决方案推动了该领域的发展,并为实时应用指明了方向。这种方法允许车辆在高速公路上持续监控、调整位置并控制速度,从而提高安全性并加强交通控制。
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OptiFlow: Optimizing Traffic Flow in ITS With Improved Cluster Routing
Intelligent Transport Systems (ITS) rely heavily on Vehicular Ad hoc Networks (VANET) to facilitate effective communication, especially Vehicle-to-Everything (V2X) communication. However, current research has identified challenges in node management, security, and routing within VANET, calling for bespoke solutions to address these issues. This study introduces an innovative cluster-based routing strategy using Enhanced Slap Swarm Optimization (ESSO) and Evaluation with Mixed Data Multi-criteria Decision-Making (EVAmix MCDM) Method tailored to optimize routing in V2X communication. Unlike existing meta-heuristic methods, which often face slow convergence, premature convergence, and local optima stability, the proposed approach demonstrates striking results. Notably, it enhances throughput by 6278 kbps, elevates the Packet Delivery Ratio (PDR) by 95.77 $\%$ , and reduces end-to-end delay by 1856ms in the 300th iteration, outperforming existing cluster routing methodologies. Our findings suggest a substantial leap toward surmounting the existing challenges in V2X communication. This innovative solution advances the field and sets a course for real-time applications. This approach allows vehicles to continually monitor, adjust their position, and control their speed on highways, enhancing safety and traffic control.
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
期刊最新文献
Enhancing AAV-to-Ground Communication Security With the Proceed-Hover-Return (PHR) Approach OptiFlow: Optimizing Traffic Flow in ITS With Improved Cluster Routing Efficient Modeling of Interest Forwarding in Information Centric Vehicular Networks Multi-Agent Deep Reinforcement Learning Based Optimizing Joint 3D Trajectories and Phase Shifts in RIS-Assisted UAV-Enabled Wireless Communications Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks
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