FOLLOWUS
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
Beijing Anqi Zhilian Technology Co., Ltd., Beijing 100000, China
Department of Computer Science, Wayne State University, Detroit 48202, USA
[ "Xiao-hong ZHANG, E-mail: xh.zhang@hpu.edu.cn" ]
Jian-ji REN, E-mail:renjianji@hpu.edu.cn
Published:2019-10,
Received:11 March 2019,
Revised:10 October 2019,
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XIAO-HONG ZHANG, KAI QIAN, JIAN-JI REN, et al. Measurement and analysis of content diffusion characteristics in opportunity environments with Spark. [J]. Frontiers of information technology & electronic engineering, 2019, 20(10): 1404-1414.
XIAO-HONG ZHANG, KAI QIAN, JIAN-JI REN, et al. Measurement and analysis of content diffusion characteristics in opportunity environments with Spark. [J]. Frontiers of information technology & electronic engineering, 2019, 20(10): 1404-1414. DOI: 10.1631/FITEE.1900137.
机会网络为分流移动网络中由流行内容共享引起的巨大负载提供了机会。分析真实机会环境中的内容传播特征可以为负载分流决策提供重要线索。然而,由于从真实机会环境中收集数据并非易事,相关工作非常有限。本文以致力于内容共享的移动应用"闪传"为研究对象,从该应用用户构成的真实机会网络中搜集数据并分析。为发现内容传播特征,本文从传播规模和速度、内容类型等方面展开分析。分析结果表明,文件传输具有明显周期性,只有很少一部分文件能广泛传播,且移动应用类文件比其他类型文件更易成为流行文件。本文还提出一种有助于预测流行文件的最大化文件传播规模的方法。实验结果证实了该方法的有效性和有用性。
Opportunity networks provide a chance to offload the tremendous cellular traffic generated by sharing popular content on mobile networks. Analyzing the content spread characteristics in real opportunity environments can discover important clues for traffic offloading decision making. However
relevant published work is very limited since it is not easy to collect data from real environments. In this study
we elaborate the analysis on the dataset collected from a real opportunity environment formed by the users of Xender
which is one of the leading mobile applications for content sharing. To discover content transmission characteristics
scale
speed
and type analyses are implemented on the dataset. The analysis results show that file transmission has obvious periodicity
that only a very small fraction of files spread widely
and that application files have much higher probability to be popular than other files. We also propose a solution to maximize file spread scales
which is very helpful for forecasting popular files. The experimental results verify the effectiveness and usefulness of our solution.
内容传播设备通信机会网络线性阈值模型
Content disseminationDevice-to-device communicationOpportunity networkLinear threshold model
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