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Placement Optimization for Advertisement Dissemination in Smart City

机译:智慧城市中广告传播的布局优化

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This paper studies a promising application in Vehicular Cyber-Physical Systems (VCPS) called roadside advertisement dissemination. Its application involves three elements: the drivers in the vehicles, Roadside Access Points (RAPs), and shopkeepers. The shopkeeper wants to attract as many customers as possible by using RAPs to disseminate advertisements to the passing vehicles. Upon receiving an advertisement, the driver might detour towards the shop, depending on the detour distance. Given a fixed number of RAPs and the traffic distribution, our goal is to optimize the RAP placement for the shopkeeper to maximally attract potential customers. This application is a non-trivial extension of traditional coverage problems, the difference being that RAPs are used to cover the traffic flows. RAP placement algorithms pose complex trade-offs. If we place RAPs at locations that can provide small detour distances to attract more customers, these locations may not necessarily be located in heavy traffic regions. While heavy traffic regions cover more flows, they might cause large detour distances, making shopping less attractive to customers. To balance the above trade-off, bounded RAP placement algorithms are proposed with respect to submodular and non-submodular scenarios. Since real-world traffic distributions exhibit unique patterns, here we further consider the Manhattan grid scenario and propose improved solutions. Extensive real trace-driven experiments validate the competitive performances of the proposed algorithms.
机译:本文研究了在汽车网络物理系统(VCPS)中称为路边广告传播的有前途的应用。它的应用涉及三个要素:车辆中的驾驶员,路边访问点(RAP)和店主。店主希望通过使用RAP将广告传播到经过的车辆中来吸引尽可能多的顾客。收到广告后,驾驶员可能会绕道而行,这取决于绕道的距离。在给定固定数量的RAP和流量分配的情况下,我们的目标是为店主优化RAP放置,以最大程度地吸引潜在客户。此应用程序是传统覆盖问题的重要扩展,不同之处在于使用RAP来覆盖流量。 RAP放置算法带来了复杂的权衡。如果我们将RAP放置在可以提供较小de回距离以吸引更多客户的位置,则这些位置可能不一定位于交通繁忙的地区。虽然交通拥挤的地区覆盖了更多的人流,但它们可能会导致绕行距离较大,从而使购物对客户的吸引力降低。为了平衡上述折衷,针对子模块和非子模块方案提出了有界RAP放置算法。由于现实世界中的交通分布具有独特的模式,因此在此我们进一步考虑曼哈顿网格方案并提出改进的解决方案。大量的实际跟踪驱动实验验证了所提出算法的竞争性能。

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