When Vehicular Fog Computing Meets Autonomous Driving: Computational Resource Management and Task Offloading
When Vehicular Fog Computing Meets Autonomous Driving: Computational Resource Management and Task Offloading
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DOI：
10.1109/mnet.001.1900527
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发表时间：
2020-11
期刊：
影响因子：
9.3
通讯作者：
Zhenyu Zhou;Haijun Liao;Xiaoyan Wang;S. Mumtaz;Jonathan Rodriguez
中科院分区：
文献类型：
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作者：
Zhenyu Zhou;Haijun Liao;Xiaoyan Wang;S. Mumtaz;Jonathan Rodriguez
自动驾驶有可能使交通系统更安全、更环保、更高效。要实现自动驾驶，需要大量的车载前沿应用，如增强现实、动态路径规划和认知驾驶系统，这些应用需要大量的计算资源和接近实时的响应。为了应对这种新的模式，车载雾计算(VFC)最近应运而生，它将计算从拥挤的基站(或云服务器)迁移到附近计算资源未得到充分利用的车辆。在VFC中，信息不对称和不确定性下的服务器招募和任务分流策略的设计提出了新的技术挑战。在本文中，我们提出了一个两阶段的VFC框架来应对这些挑战。该框架由基于契约理论的车载计算资源管理机制和基于匹配学习的任务卸载机制组成。仿真结果表明，该框架在资源利用效率和任务卸载延迟方面都能提高VFC的性能。
Autonomous driving has the potential to make transportation systems safer, greener and more efficient. To realize autonomous driving, a great deal of in-car cutting-edge applications such as augmented reality, dynamic path planning and cognitive driving systems are required, which need significant computational resources and near realtime response. To cope with this new paradigm, vehicular fog computing (VFC) has emerged recently, which migrates the computing from congested base stations (or cloud servers) to nearby vehicles with under-utilized computational resources. in VFC, the designs of server recruitment and task offloading strategies under information asymmetry and uncertainty pose new technical challenges. in this article, we propose a two-stage VFC framework to address these challenges. The framework consists of a contract theory based vehicular computational resource management mechanism, and a matching-learning based task offloading mechanism. Simulation results demonstrate that the proposed framework can boost the performance of VFC in terms of resource utilization efficiency and task offloading delay.