科研成果详情

题名Data gathering framework based on fog computing paradigm in VANETs
作者
发表日期2017
会议名称1st Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data
会议录名称Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
会议录编者Song, S; Renz, M; Moon, YS
ISBN978-3-319-69781-9; 978-3-319-69780-2
ISSN0302-9743
卷号10612 LNCS
页码227-236
会议日期JUL 07-09, 2017
会议地点Beijing, CHINA
摘要

Vehicular nodes are equipped with more and more sensing units, and large amount of sensing data are generated. This makes the data gathering and monitoring is a challenging problem in VANETs. In this paper we first present an sensing and data gathering framework through the concept of fog computing in VANETs, then we propose an event-based data gathering scheme based on this framework that adaptively adjusts the threshold to upload suitable amount of data for decision making, while at the same time suppress unnecessary message transmissions. Preliminary experiments demonstrate the effectiveness of the proposed algorithm in vehicular sensing applications.

关键词Data gathering framework Fog nodes VANETs
DOI10.1007/978-3-319-69781-9_22
URL查看来源
收录类别CPCI-S
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号WOS:000454479300022
Scopus入藏号2-s2.0-85034602554
引用统计
被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/7244
专题个人在本单位外知识产出
通讯作者Lai, Yongxuan
作者单位
1.Department of Software Engineering,Software College,Xiamen University,Xiamen,361005,China
2.College of Computer Science and Technology, Huaqiao University, Xiamen, 360000, China
3.College of Aerospace Engineering, Xiamen University, Xiamen, 361005, China
推荐引用方式
GB/T 7714
Lai, Yongxuan,Zhang, Lu,Wang, Tianet al. Data gathering framework based on fog computing paradigm in VANETs[C]//Song, S; Renz, M; Moon, YS, 2017: 227-236.
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