An optimized framework for VANET routing: A multi-objective hybrid model for data synchronization with digital twin

Madhuri Husan Badole, Anuradha D. Thakare
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引用次数: 0

Abstract

The utilization of Digital Twin technology allows for the simulation of network behavior, anticipating traffic surges, and implementing efficient traffic routing strategies to prevent congestion. This enhances network efficiency and improves overall speed. However, VANETs (Vehicular Ad-Hoc Networks) pose unique challenges due to their dynamic nature and frequent network disconnects. Developing and implementing effective VANET routing protocols becomes complex considering these factors. To address these challenges, a novel hybrid optimization model is proposed in this research. The model comprises optimal Cluster Head (CH) selection for data transmission. The clustering of mobile nodes is initially performed, but ensuring consistency in fast-paced environments remains a significant challenge. Therefore, the selection of the most suitable node as the CH is crucial. This research introduces a novel route selection mechanism that focuses on optimal CH selection. Multiple objectives such as mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead are considered in the CH selection process. To determine the ideal CH from a pool of potential candidates, a new hybrid optimization model called Hunger's Foraging Behavior Customized Honey Badger Optimization (HFCHBO) is introduced. The HFCHBO combines the standard Honey Badger Algorithm (HBA) with Hunger Games Search (HGS). This hybrid model effectively formulates successful routing paths for data transmission between vehicles and the CH to the Base Station (BS). By combining these two approaches, the HFCHBO model aims to overcome the limitations of traditional clustering algorithms in ensuring consistent performance in dynamic environments. The proposed route selection mechanism incorporates multiple objectives to evaluate the performance of potential CHs, including mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead. To facilitate data transmission between vehicles and the CH to the Base Station (BS), the HFCHBO model formulates successful routing paths. By utilizing the simulation capabilities of the Digital Twin technology, the model analyzes the network behavior, predicts traffic patterns, and makes informed decisions on routing strategies.

VANET路由优化框架:一个具有数字孪生的多目标混合数据同步模型
数字孪生技术的使用允许模拟网络行为,预测流量激增,并实施有效的流量路由策略来防止拥塞。这提高了网络效率并提高了整体速度。然而,VANET(车载自组织网络)由于其动态特性和频繁的网络断开而带来了独特的挑战。考虑到这些因素,开发和实现有效的VANET路由协议变得复杂。为了应对这些挑战,本研究提出了一种新的混合优化模型。该模型包括用于数据传输的最优簇头(CH)选择。移动节点的集群最初是执行的,但确保快节奏环境中的一致性仍然是一个重大挑战。因此,选择最合适的节点作为CH是至关重要的。本研究引入了一种新的路由选择机制,该机制侧重于最优CH选择。在CH选择过程中考虑了多个目标,如平均路由负载、分组传递率、吞吐量、端到端延迟和控制分组开销。为了从潜在的候选者库中确定理想的CH,引入了一种新的混合优化模型,称为饥饿觅食行为定制蜜獾优化(HFCHBO)。HFCHBO结合了标准的蜜獾算法(HBA)和饥饿游戏搜索(HGS)。该混合模型有效地制定了用于车辆和CH之间到基站(BS)的数据传输的成功路由路径。通过将这两种方法相结合,HFCHBO模型旨在克服传统聚类算法在确保动态环境中一致性能方面的局限性。所提出的路由选择机制结合了多个目标来评估潜在CH的性能,包括平均路由负载、分组传递率、吞吐量、端到端延迟和控制分组开销。为了促进车辆和CH到基站(BS)之间的数据传输,HFCHBO模型制定了成功的路由路径。该模型利用数字孪生技术的模拟能力,分析网络行为,预测流量模式,并对路由策略做出明智的决策。
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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