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Resource efficient allocation of fog nodes for faster vehicular OTA updates

机译:用于更快的车辆OTA更新的FOG节点的资源有效分配

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Despite reduced network latency and resilience, fog computing has not been leveraged for vehicular Over-the-Air (OTA) updates. Due to vehicle mobility and traffic, the resource utilization of fog nodes is almost non-deterministic, which increases the delay in communication and handover. In this paper, we propose an approach for distributing fog nodes by analyzing the vehicular traffic pattern in a region. The proposed method: (a) finds the optimal number of fog nodes for a specific time interval based on the traffic pattern of a region and (b) maximizes the net reserve resources enabling specific fog nodes. To do so, we use the k-means algorithm to identify traffic load and distribute the fog nodes using our proposed algorithm to maximize fog resource utilization. We present a case study of OTA updates that considers vehicle mobility, data transmission rate, propagation delay and handover delay to predict the required update time. The experimental results demonstrate that the proposed method of fog node allocation extends the net reserve resources by 30.92% on an average, and reduces the OTA update time.
机译:尽管网络延迟和弹性减少,但雾计算尚未为车辆过空气(OTA)更新而杠杆化。由于车辆移动和流量,雾节点的资源利用几乎是非确定性的,这增加了通信和切换的延迟。在本文中,我们通过分析区域中的车辆交通模式来提出一种用于分发雾节点的方法。所提出的方法:(a)基于区域的业务模式找到特定时间间隔的最佳数量的雾节点,并且(b)最大化净储备资源启用特定的雾节点。为此,我们使用K-Means算法使用我们所提出的算法来识别流量负载并分发雾节点以最大化FOG资源利用率。我们展示了考虑车辆移动性,数据传输速率,传播延迟和切换延迟的OTA更新的案例研究,以预测所需的更新时间。实验结果表明,雾节点分配的提议方法平均将净储备资源扩展了30.92%,并减少了OTA更新时间。

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