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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Downlink Traffic Scheduling in Green Vehicular Roadside Infrastructure
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Downlink Traffic Scheduling in Green Vehicular Roadside Infrastructure

机译：绿色车辆路边基础设施中的下行链路流量调度

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In this paper we consider the problem of scheduling for energy-efficient roadside infrastructure. In certain scenarios, vehicle locations can be predicted with a high degree of accuracy, and this information can be used to reduce downlink infrastructure-to-vehicle energy communication costs. Offline scheduling results are first presented that provide lower bounds on the energy needed to satisfy arriving vehicular communication requirements. We show that the packet-based scheduling case can be formulated as a generalization of the classical single-machine job scheduling problem with a tardiness penalty, which is referred to as α-Earliness-Tardiness. A proof is given that shows that even under a simple distance-dependent exponential radio path loss assumption, the problem is NP-complete. The remainder of the paper then focuses on timeslot-based scheduling. We formulate this problem as a Mixed-Integer Linear Program (MILP) that is shown to be solvable in polynomial time using a proposed minimum cost flow graph construction. Three energy-efficient online traffic scheduling algorithms are then introduced for common vehicular scenarios where vehicle position is strongly deterministic. The first, i.e., Greedy Minimum Cost Flow (GMCF), is motivated by our minimum cost flow graph formulation. The other two algorithms have reduced complexity compared with GMCF. The Nearest Fastest Set (NFS) scheduler uses vehicle location and velocity inputs to dynamically schedule communication activity. The Static Scheduler (SS) performs the same task using a simple position-based weighting function. Results from a variety of experiments show that the proposed scheduling algorithms perform well when compared with the energy lower bounds in vehicular situations where path loss has a dominant deterministic component so that energy costs can be estimated. Our results also show that near-optimal results are possible but come with increased computation times compared with our heuristic - lgorithms.
机译：在本文中，我们考虑了节能路边基础设施的调度问题。在某些情况下，可以高度准确地预测车辆位置，并且可以使用此信息来减少下行链路基础结构到车辆的能源通信成本。首先介绍了离线调度结果，该结果提供了满足到达的车辆通信要求所需能量的下限。我们表明，基于数据包的调度情况可以表述为具有拖尾惩罚的经典单机作业调度问题的推广，这被称为α-提前/拖后。给出的证据表明，即使在简单的距离相关指数无线路径损耗假设下，问题也是NP完全的。然后，本文的其余部分将重点介绍基于时隙的调度。我们将此问题公式化为混合整数线性程序（MILP），使用拟议的最低成本流程图构造，该程序可在多项式时间内求解。然后针对常见的车辆场景引入了三种高能效的在线交通调度算法，在这些场景中，车辆位置具有很强的确定性。第一种，即贪婪的最低成本流（GMCF），是由我们的最低成本流图公式引起的。与GMCF相比，其他两种算法降低了复杂度。最近最快设置（NFS）调度程序使用车辆位置和速度输入来动态调度通信活动。静态调度程序（SS）使用简单的基于位置的加权功能执行相同的任务。各种实验的结果表明，与在路径损耗具有决定性因素的主要车辆状况下的能量下限相比，所提出的调度算法性能良好，因此可以估算能源成本。我们的结果还表明，接近最佳结果是可能的，但是与我们的启发式算法相比，计算时间增加了。

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