Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication, sensing and computing

Xinrui Gu, Shengfeng Wang, Zhiqing Wei, Zhiyong Feng
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Abstract

The integration of communications, sensing and computing (I-CSC) has significant applications in vehicular ad hoc networks (VANETs). A roadside unit (RSU) plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication. Due to the constraints of limited resources, RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method. However, direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue. In this paper, we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks. The renormalization method is compared with two deployment schemes: genetic algorithm (GA) and memetic framework-based optimal RSU deployment (MFRD), to verify the improvement of communication performance. Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

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集群式 RSU 部署策略,用于集成通信、传感和计算功能的车载 Ad Hoc 网络
通信、传感和计算一体化(I-CSC)在车载特设网络(VANET)中有着重要的应用。路边单元(RSU)在 I-CSC 中发挥着重要作用,它在车辆通信中承担着信息传输和边缘计算等功能。由于资源有限,RSU 无法实现全覆盖,在路网分层结构的关键簇头部署 RSU 是一种有效的管理方法。然而,在 VANET 中直接提取分层结构进行资源分配是一个尚未解决的问题。在本文中,我们提出了一种基于信息流和地理位置的网络重归一化方法,在路网中分层部署 RSU。我们将重归一化方法与两种部署方案:遗传算法(GA)和基于记忆框架的 RSU 优化部署(MFRD)进行了比较,以验证通信性能的提高。结果表明,就 RSU 覆盖范围和信息接收率而言,重归一化方法优于其他方案。
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Editorial Board Editorial Board Secure performance comparison for NOMA: Reconfigurable intelligent surface or amplify-and-forward relay? Editorial Board Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing
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