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Wireless Communications and Mobile Computing
Volume 2018, Article ID 6353714, 25 pages
Research Article

Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks

1School of Information Science and Engineering, Central South University, Changsha 410083, China
2School of Information Technology in Education, South China Normal University, Guangzhou 510631, China
3College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian 361021, China

Correspondence should be addressed to Anfeng Liu; nc.ude.usc.liam@uilgnefa

Received 13 December 2017; Accepted 14 February 2018; Published 27 March 2018

Academic Editor: Tao Han

Copyright © 2018 Yingying Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.