Characterizing Flow, Application, and User Behavior in Mobile Networks: A Framework for Mobile Big Data

Yuanyuan Qiao, Zhizhuang Xing, Zubair Md Fadlullah, Jie Yang, Nei Kato

研究成果: Article

13 引用 (Scopus)

抜粋

The recent explosion of data traffic calls for specialized systems to monitor the status of networks. Traditionally, Internet service providers collect and analyze IP flow data as they present an aggregated view of traffic. In the era of mobile big data, new approaches are required to address new challenges regarding the flow characterization in the next generation wireless networks. In this article, we propose a framework for mobile big data, referred to as FMBD, which provides massive data traffic collection, storage, processing, analysis, and management functions, to cope with the tremendous amount of data traffic. In particular, by analyzing the specific characteristics of the mobile big data from flow, application, and user behavior, such as high volume, diversity of applications, and spatiooral distribution, our proposed FMBD demonstrates its capability to offer real data-based advice to address new challenges for future wireless networks from the viewpoints of both operators and individuals. Tested by real mobile big data, FMBD has been operational for more than five years, and can be generalized to other environments with massive data traffic or big data. Introduction .

元の言語English
ページ(範囲)40-49
ページ数10
ジャーナルIEEE Wireless Communications
25
発行部数1
DOI
出版物ステータスPublished - 2018 2

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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