论文标题
公平：用于具有汽车边缘计算的智能车辆的公正资源分配
FAIR: Towards Impartial Resource Allocation for Intelligent Vehicles with Automotive Edge Computing
论文作者
论文摘要
新兴的车辆连接应用程序，例如合作自动驾驶和交叉路口碰撞警告，具有提高驾驶安全性的巨大潜力，在这些应用程序中，车辆可以通过周围车辆和路边基础设施共享各种板载传感器收集的数据。通过传统的无线通信网络传输和处理这些大量的感官数据为汽车边缘计算带来了新的挑战。在这项工作中，我们解决了在上行链路和下行连接的传统不对称网络资源分配的问题，这些问题可能会大大降低车辆连接应用的性能。公平的端到端汽车边缘网络系统提议为具有边缘计算的智能车辆提供快速，可扩展和公正的连接服务，该服务可用于任何交通场景和道路拓扑。公平的核心是我们提出的对称网络资源分配算法，该算法部署在边缘服务器和配备智能车辆的服务适应算法。进行广泛的模拟是为了通过利用现实世界流量数据集来验证我们提出的公平。模拟结果表明，在各种交通场景和道路拓扑中，公平的表现优于现有解决方案。
The emerging vehicular connected applications, such as cooperative automated driving and intersection collision warning, show great potentials to improve the driving safety, where vehicles can share the data collected by a variety of on-board sensors with surrounding vehicles and roadside infrastructures. Transmitting and processing this huge amount of sensory data introduces new challenges for automotive edge computing with traditional wireless communication networks. In this work, we address the problem of traditional asymmetrical network resource allocation for uplink and downlink connections that can significantly degrade the performance of vehicular connected applications. An end-to-end automotive edge networking system, FAIR, is proposed to provide fast, scalable, and impartial connected services for intelligent vehicles with edge computing, which can be applied to any traffic scenes and road topology. The core of FAIR is our proposed symmetrical network resource allocation algorithm deployed at edge servers and service adaptation algorithm equipped on intelligent vehicles. Extensive simulations are conducted to validate our proposed FAIR by leveraging real-world traffic dataset. Simulation results demonstrate that FAIR outperforms existing solutions in a variety of traffic scenes and road topology.
