Access Point Recruitment in a Vehicular Cognitive Capability Harvesting Network: How Much Data Can Be Uploaded?
Access Point Recruitment in a Vehicular Cognitive Capability Harvesting Network: How Much Data Can Be Uploaded?
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DOI：
10.1109/tvt.2018.2803762
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发表时间：
2018-02
影响因子：
6.8
通讯作者：
Haichuan Ding;Chi Zhang;B. Lorenzo;Yuguang Fang
中科院分区：
文献类型：
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作者：
Haichuan Ding;Chi Zhang;B. Lorenzo;Yuguang Fang
为了有效应对新兴物联网（IoT）和智慧城市应用带来的爆炸式流量，我们最近设计了一种车辆认知能力采集网络架构，其中虚拟服务提供商（VSP）协调配备强大通信设备（即认知无线电路由器）的车辆，帮助各种终端设备通过部署或部署的路边接入点（ap）将数据上传到数据网络。为了使AP招聘具有成本效益，VSP有必要了解每个AP可以提供什么。因此，在本文中，通过将车辆到达过程建模为泊松过程，我们分析了AP实现的最大长期上传吞吐量。由于AP覆盖范围内的争用，每辆车上传的数据量是相关的，这使得我们的分析变得困难。为了解决这一挑战，我们将考虑的问题重新表述为更新奖励过程，这使我们能够推导出最大长期上传吞吐量的封闭形式表达式。我们通过广泛的模拟来验证我们的分析结果，这可以为我们提供有用的AP招聘见解。
To effectively deal with exploding traffic from emerging Internet of things (IoT) and smart cities applications, we have recently designed a vehicular cognitive capability harvesting network architecture where a virtual service provider (VSP) coordinates vehicles equipped with powerful communication devices, namely cognitive radio routers, to help various end devices upload their data to data networks via deployed or recruited roadside access points (APs). To make the AP recruitment cost-effective, it is necessary for the VSP to learn what each AP can offer. Thus, in this paper, by modeling the vehicle arrival process as a Poisson process, we analyze the maximum long-term upload throughput achieved with an AP. Due to the contention inside the coverage of the AP, the amount of data uploaded by each vehicle is correlated, which makes our analysis difficult. To address this challenge, we reformulate the considered problem as a renewal reward process, which allows us to derive the closed-form expression for the maximum long-term upload throughput. We validate our analytical results via extensive simulations, which can offer us useful insights on AP recruitment.