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Towards scalable, fair and robust data dissemination via cooperative vehicular communications

机译:通过协作性车辆通信实现可扩展,公平和健壮的数据分发

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Recent advances in infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are envisioned to enable a variety of emerging applications in vehicular networks, where it is imperative to provide efficient data services via cooperative vehicular communications. In this work, we present the data dissemination system via cooperative I2V and V2V communications. We formulate the problem by investigating both the communication constraint and the application requirement on data dissemination. The goal is to maximize the system performance by exploiting the joint effects of I2V and V2V communications. On this basis, we propose an on-line scheduling algorithm to enable scalable, fair and robust data dissemination. The algorithm makes scheduling decisions by transforming the data dissemination problem to the maximum weighted independent set (MWIS) problem and approximately solving MWIS using a greedy method. We build the simulation model based on realistic traffic and communication characteristics. A comprehensive simulation study demonstrates that the proposed solution is able to effectively strike a balance between I2V and V2V data services and maximize system performance in terms of scalability, fairness and robustness.
机译:构想了基础设施对车辆(I2V)和车辆对车辆(V2V)通信的最新进展,以实现在车辆网络中的各种新兴应用,其中必须通过协作车辆通信提供有效的数据服务。在这项工作中,我们介绍了通过I2V和V2V协作通信的数据分发系统。我们通过研究通信约束和数据分发的应用需求来制定问题。目的是通过利用I2V和V2V通信的联合效应来最大化系统性能。在此基础上,我们提出了一种在线调度算法,以实现可扩展,公平和健壮的数据分发。该算法通过将数据分发问题转换为最大加权独立集(MWIS)问题并使用贪婪方法近似求解MWIS来做出调度决策。我们基于现实的交通和通信特征构建仿真模型。全面的仿真研究表明,提出的解决方案能够有效地在I2V和V2V数据服务之间取得平衡,并在可伸缩性,公平性和鲁棒性方面最大化系统性能。

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