Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks
Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks
复制标题
基于质量利用感知的车辆通信网络数据收集
DOI：
10.1155/2018/6353714
复制
发表时间：
2018
影响因子：
--
通讯作者：
Wang Tian
中科院分区：
文献类型：
--
作者：
Ren Yingying;Liu Anfeng;Zhao Ming;Huang Changqin;Wang Tian
车载通信网络可以采用具有参与式感知的移动的智能传感设备来收集数据，可以是基于大数据构建各种应用的有效且经济的方式。然而，车载通信网络迫切需要高质量的数据采集，面临着许多挑战。因此，本文提出了一个细粒度的数据收集框架来科普这些新的挑战。与传统的数据收集方法集中于如何收集足够的数据以满足应用需求不同，针对车载通信网络提出了一种质量利用感知数据收集（QUADG）方案，以收集最合适的数据并最好地满足应用的多维需求（主要包括数据收集的数量、质量和成本）。在QUADG方案中，数据感知是细粒度的，其中数据收集时间和数据收集区域被划分为非常细的粒度。质量利用率（Quality Utilization，QU）是一个衡量传感器数据质量与系统成本之比的指标。提出了三种数据采集算法。第一种算法是通过最大化QU来保证已经获得指定数量的感知数据的应用能够最小化成本和最大化数据质量。第二种算法是保证获得两个应用请求（数据收集的数量和质量，或者数据收集的数量和成本）的应用能够最大化QU。第三个算法是保证同时满足数据采集数量、质量和成本要求的应用程序能够最大化QU。最后，我们通过大量的模拟，很好地证明了我们的计划的有效性，我们提出的计划与现有的计划进行比较。
The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine-grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine-grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.