Mobile Big Data: The Fuel for Data-Driven Wireless

Xiang Cheng, Luoyang Fang, Liuqing Yang, Shuguang Cui

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


In the past decade, the smart phone evolution has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications, leading to an unprecedented mobile data volume generated from the mobile devices, content servers, and network operators, which are mainly nonstructured. In this big data era, such nonstructured data fragments are pieced together such that, drastically differing from the traditional practice where services determine and define the data, data is becoming a proactive entity that may drive and even create new services. Compared with the so-termed 5V characteristics of generic big data, namely volume, variety, velocity, veracity, and value, mobile big data is distinct in its unique multidimensional, personalized, multisensory, and real-time features. In this survey, we provide in-depth and comprehensive coverage on the features, sources and applications of mobile big data, as well as the current state-of-the-art, challenges and opportunities for research and development in this field, with an emphasis on the user modeling, infrastructure supporting, data management, and knowledge discovery aspects.

Original languageEnglish (US)
Article number7945539
Pages (from-to)1489-1516
Number of pages28
JournalIEEE Internet of Things Journal
Issue number5
StatePublished - Oct 2017
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received March 10, 2017; revised May 10, 2017; accepted May 27, 2017. Date of publication June 9, 2017; date of current version October 9, 2017. This work was supported in part by the National Natural Science Foundation of China under Grant 61622101, Grant 61571020, Grant 61328102, and Grant 61629101, in part by the Ministry National Key Research and Development Project under Grant 2016YFE0123100, in part by the Open Research Fund of National Mobile Communications Research Laboratory under Grant 2016D03, in part by the Southeast University, in part by the National Science Foundation under Grant CNS-1343189/1343155, Grant DMS-1521746/1622433, Grant AST-1547436, and Grant ECCS-1508051/1659025, in part by the DoD under Grant HDTRA1-13-1-0029, and in part by the Shenzhen Fundamental Research Fund under Grant KQTD2015033114415450. (Corresponding author: Xiang Cheng.) X. Cheng is with the State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China, and also with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210018, China (e-mail:

Publisher Copyright:
© 2014 IEEE.


  • Big data applications
  • data analysis
  • data mining
  • mobile communication
  • mobile computing
  • mobile learning
  • pervasive computing


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