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Deadline-constrained data aggregation scheduling in urban vehicular networks

机译：城市车辆网络中受时间限制的数据聚合调度

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摘要

With the rapid increasing scale of Intelligent Transportation System (ITS) and vehicular network, our quality of life are significantly improved, especially traveling in urban area. To supply a large amount of real-time ITS applications, such as navigation and automatic driving, aggregation delay should be controlled in a small range so that more data might be dropped during aggregation. State-of-the-art data aggregation approaches determine to aggregate and forward data based on the instantaneous link status. However, wireless link status in a period should be taken into account to reduce dropped data in a long term. Given the estimated further link status, this paper formulates deadline-constrained data aggregation scheduling problem into a nonlinear integer programming problem. We design an efficient algorithm, DASV, to accelerate branching the solution space because most of the variables appear only once in the objective function or constraints. Based on taxi trajectory data in San Francisco, experimental results demonstrate the proposed DASV outperforms state-of-art methods 22.7% in terms of aggregation ratio.
机译：随着智能交通系统（ITS）和车辆网络规模的迅速增加，我们的生活质量得到了显着改善，尤其是在城市地区。为了提供大量的实时ITS应用程序（例如导航和自动驾驶），应将聚合延迟控制在较小范围内，以便在聚合期间可以丢弃更多数据。最新的数据聚合方法基于瞬时链接状态确定聚合和转发数据。但是，应考虑一段时间内的无线链路状态，以长期减少丢失的数据。给定估计的进一步的链路状态，本文将截止期限约束的数据聚合调度问题表述为非线性整数规划问题。我们设计了一种有效的算法DASV，以加速分支求解空间，因为大多数变量在目标函数或约束中仅出现一次。根据旧金山的出租车轨迹数据，实验结果表明，所提出的DASV在聚集率方面优于最新方法22.7％。

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