Characterizing User Behavior in Mobile Internet

Smart devices bring us the ubiquitous mobile accessing to Internet, making mobile Internet grow rapidly. Using the mobile traffic data collected at core metropolitan 2G and 3G networks of China over a week, this paper studies the mobile user behavior from three aspects: 1) data usage; 2) mobility pattern; and 3) application usage. We classify mobile users into different groups to study the resource consumption in mobile Internet. We observe that traffic heavy users and high mobility users tend to consume massive data and radio resources simultaneously. Both the data usage and the mobility pattern are closely related to the application access behavior of the users. Users can be clustered through their application usage behavior, and application categories can be identified by the ways to attract the users. Our analysis provides an comprehensive understanding of user behavior in mobile Internet, which may be used by network operators to design appropriate mechanisms in resource provision and mobility management for resource consumers based on different categories of applications.

[1]  Rodrigo de Oliveira,et al.  What's up with whatsapp?: comparing mobile instant messaging behaviors with traditional SMS , 2013, MobileHCI '13.

[2]  Harry Bouwman,et al.  Analysis of users and non-users of smartphone applications , 2010, Telematics Informatics.

[3]  Sang Pil Han,et al.  An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet , 2011, Manag. Sci..

[4]  Clayton Shepard,et al.  Characterizing web use on smartphones , 2012, CHI.

[5]  Jing Ma,et al.  An Empirical Investigation of Filter Attribute Selection Techniques for High-Speed Network Traffic Flow Classification , 2012, Wirel. Pers. Commun..

[6]  Walter Willinger,et al.  Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference , 2011, IMC 2011.

[7]  Deborah Estrin,et al.  A first look at traffic on smartphones , 2010, IMC '10.

[8]  Lusheng Ji,et al.  Characterizing geospatial dynamics of application usage in a 3G cellular data network , 2012, 2012 Proceedings IEEE INFOCOM.

[9]  Inderjit S. Dhillon,et al.  Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.

[10]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[11]  Nitin H. Vaidya,et al.  Proceedings of the sixteenth annual international conference on Mobile computing and networking , 2010, MobiCom 2010.

[12]  Lei Zhen-ming,et al.  Structural analysis of complex networks from the mobile Internet , 2013 .

[13]  Lixin Gao,et al.  Profiling users in a 3g network using hourglass co-clustering , 2010, MobiCom.

[14]  Zhifeng Zhao,et al.  Human Mobility Patterns in Cellular Networks , 2013, IEEE Communications Letters.

[15]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[16]  Qiang Xu,et al.  Identifying diverse usage behaviors of smartphone apps , 2011, IMC '11.

[17]  Samir Ranjan Das,et al.  Understanding traffic dynamics in cellular data networks , 2011, 2011 Proceedings IEEE INFOCOM.

[18]  James Won-Ki Hong,et al.  Characteristic analysis of internet traffic from the perspective of flows , 2006, Comput. Commun..

[19]  Nuria Oliver,et al.  Understanding mobile web and mobile search use in today's dynamic mobile landscape , 2011, Mobile HCI.

[20]  Scott P. Robertson,et al.  Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 1991 .

[21]  Bruce A. Mah,et al.  An empirical model of HTTP network traffic , 1997, Proceedings of INFOCOM '97.

[22]  Aleksandar Kuzmanovic,et al.  Measuring serendipity: connecting people, locations and interests in a mobile 3G network , 2009, IMC '09.

[23]  Shobha Venkataraman,et al.  Characterizing data usage patterns in a large cellular network , 2012, CellNet '12.

[24]  Barry Smyth,et al.  Mobile information access: A study of emerging search behavior on the mobile Internet , 2007, TWEB.

[25]  Nei Kato,et al.  On Characterizing Peer-to-Peer Streaming Traffic , 2013, IEEE Journal on Selected Areas in Communications.

[26]  Paramvir Bahl,et al.  Anatomizing application performance differences on smartphones , 2010, MobiSys '10.

[27]  Lionel M. Ni,et al.  An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data , 2012, UbiComp.

[28]  Guohong Cao,et al.  Characterizing Data Services in a 3G Network: Usage, Mobility and Access Issues , 2011, 2011 IEEE International Conference on Communications (ICC).

[29]  Anja Feldmann,et al.  On dominant characteristics of residential broadband internet traffic , 2009, IMC '09.

[30]  Hannu Verkasalo Analysis of Smartphone User Behavior , 2010, 2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR).