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Vehicle Mobility-Based Geographical Migration of Fog Resource for Satellite-Enabled Smart Cities

机译:基于车辆移动性的人造卫星城市雾资源的地理迁移

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The diverse applications and high-quality services in satellite-enabled smart cities have led to geographical unbalance of computation requirements. Traditional centralized cloud services and massive migration of computing tasks result in the increase of network delay and the aggravation of network congestion. Deploying fog nodes at the network edge has become a way to improve the quality of service (QoS). However, the dynamic requirements and application in various scenarios still challenge the network, resulting in geographical unbalance of computing resource demands. Nowadays, computing resources of on-board computers and devices in the Internet of Vehicles (IoV) are abundant enough to mitigate the geographical unbalances in computing power demand. Efficient usage of the natural mobility of constantly moving vehicles to solve the problems above remains an urgent need. In this paper, vehicle mobility-based geographical migration model of vehicular computing resource is established for satellite-enabled smart cities. By using the road- status-awareness of fog nodes, the status of roads is precisely quantified as the basis for vehicle mobility- based resource migration. An incentive scheme that affects the vehicle path selection through resource pricing is proposed to balance the resource requirements and to geographically allocate computing resources. Simulation results indicate that the advantages and efficiency of the proposed scheme are significant.
机译:具有卫星功能的智慧城市中的各种应用程序和高质量服务已导致计算需求在地理上失衡。传统的集中式云服务和计算任务的大量迁移导致网络延迟的增加和网络拥塞的加剧。在网络边缘部署雾节点已成为提高服务质量(QoS)的一种方法。但是,各种场景中的动态需求和应用仍然挑战着网络,导致计算资源需求的地域不平衡。如今,车联网(IoV)中车载计算机和设备的计算资源十分丰富,足以缓解计算能力需求中的地域不平衡。有效地利用不断移动的车辆的自然机动性来解决上述问题仍然是迫切需要的。本文为卫星智能城市建立了基于车辆移动性的车辆计算资源地理迁移模型。通过使用雾节点的道路状态感知,可以精确量化道路状态,将其作为基于车辆移动性的资源迁移的基础。提出了一种通过资源定价影响车辆路径选择的激励方案,以平衡资源需求并在地理上分配计算资源。仿真结果表明,该方案的优点和效率是显着的。

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