Efficient Hybrid Data Dissemination for Edge-Assisted Automated Driving
Efficient Hybrid Data Dissemination for Edge-Assisted Automated Driving
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DOI：
10.1109/jiot.2019.2946276
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发表时间：
2020-01
影响因子：
10.6
通讯作者：
Lei Yang;Lingling Zhang;Zongjian He;Jiannong Cao;Weigang Wu
中科院分区：
文献类型：
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作者：
Lei Yang;Lingling Zhang;Zongjian He;Jiannong Cao;Weigang Wu
自动驾驶服务具有容量大、位置感知、时变等特点，适合边缘缓存。然而，如果车辆根据需要直接从边缘访问内容，那么边缘上的流量将非常高，特别是在车辆密度高的区域。为了减少边缘交通流量，提出了一种车对车（V2V）和车对基础设施（V2I）的混合数据传播模型，其中边缘（基础设施）有选择地向车辆注入数据，并利用车辆网络传播数据。本文研究混合数据传播问题，即以最小的边缘交通成本和满足数据获取的时限为目标，最优地确定何时和哪辆车注入数据，以及车辆是直接从边缘还是从附近的邻居获取所需的数据。现有方法首先优先选择V2I传播，然后探索与V2I传播不冲突的V2V传播。这种方法不能充分利用V2V的优势来降低边缘的流量成本。本文提出了一种新的数据传播算法，称为混合数据传播离线算法（OFDD），该算法以优先级寻找最有利的V2V广播，然后选择可行的V2I传播。基于OFDD，我们开发了基于快照和基于预测的在线算法。我们随后进行了大量的模拟来验证所提出的算法。结果表明，我们的算法在数据采集率和流量成本方面明显优于最先进的方法。
Automatic driving services have large volume, location-aware, and time-changing contents, which are suitable to be cached by the edge. However, the traffic on the edge will be extremely high especially in the area with high vehicle density, if the vehicles directly access the contents from the edge as they demand. A hybrid data dissemination model with both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) disseminations has been proposed to reduce the traffic on the edge, in which the edge (infrastructure) selectively injects data to the vehicles and leverages the vehicle network to disseminate the data. In this article, we study the hybrid data dissemination problem, i.e., to optimally determine when and which vehicle the data are injected into, and whether the vehicle acquires the demanded data directly from the edge or from nearby neighbors, with the aim of minimizing the traffic cost on the edge and meeting the deadlines of acquiring the data. Existing approach prioritizes the selection of V2I disseminations at first and then explores the V2V disseminations which have no conflict with the V2I disseminations. This approach cannot fully take advantage of V2V to reduce the traffic cost on the edge. We propose a new data dissemination algorithm, named the offline algorithm for hybrid data dissemination (OFDD), which seeks the most beneficial V2V broadcasts with priority, and then choose feasible V2I disseminations. Based on OFDD, we develop both the snapshot and prediction-based online algorithms. We follow with extensive simulations to validate the proposed algorithms. The results show that our algorithms significantly outperform the state-of-the-art approaches in terms of data acquisition rate and traffic cost.