Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage

We investigate how individual mobile services are consumed at a national scale, by studying data collected in a 3G/4G mobile network deployed over a major European country. Through correlation and clustering analyses, our study unveils a strong heterogeneity in the demand for different mobile services, both in time and space. In particular, we show that: (i) somehow surprisingly, almost all considered services exhibit quite different temporal usage patterns; (ii) in contrast to such temporal behavior, spatial patterns are fairly uniform across all services; (iii) when looking at usage patterns at different locations, the average traffic volume per user is dependent on the urbanization level, yet its temporal dynamics are not. Our findings do not only have sociological implications, but are also relevant to the orchestration of network resources.

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

[2]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[3]  Qiang Xu,et al.  AccuLoc: practical localization of performance measurements in 3G networks , 2011, MobiSys '11.

[4]  Dario Pompili,et al.  Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges , 2016, IEEE Communications Magazine.

[5]  Bin Han,et al.  Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks , 2017, IEEE Communications Magazine.

[6]  Lusheng Ji,et al.  Characterizing and modeling internet traffic dynamics of cellular devices , 2011, SIGMETRICS '11.

[7]  A. Liu,et al.  Characterizing and modeling internet traffic dynamics of cellular devices , 2011, PERV.

[8]  Nicu Sebe,et al.  The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective , 2016, WWW.

[9]  Marco Fiore,et al.  A Tale of Ten Cities: Characterizing Signatures of Mobile Traffic in Urban Areas , 2017, IEEE Transactions on Mobile Computing.

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

[11]  Ying Zhang,et al.  Understanding the characteristics of cellular data traffic , 2012, CellNet '12.

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

[13]  George Pavlou,et al.  Adaptive Resource Management and Control in Software Defined Networks , 2015, IEEE Transactions on Network and Service Management.

[14]  Ying Zhang,et al.  Understanding the characteristics of cellular data traffic , 2012, CCRV.

[15]  Luis Gravano,et al.  k-Shape: Efficient and Accurate Clustering of Time Series , 2016, SGMD.

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

[17]  Kaigui Bian,et al.  Characterizing Smartphone Usage Patterns from Millions of Android Users , 2015, Internet Measurement Conference.

[18]  Gaogang Xie,et al.  An empirical study of the WeChat mobile instant messaging service , 2017, 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[19]  Gaogang Xie,et al.  An Empirical Analysis of a Large-scale Mobile Cloud Storage Service , 2016, Internet Measurement Conference.

[20]  Carey L. Williamson,et al.  Characterization of CDMA2000 cellular data network traffic , 2005, The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l.

[21]  Pedro Casas,et al.  Vivisecting whatsapp through large-scale measurements in mobile networks , 2014, SIGCOMM.

[22]  K. K. Ramakrishnan,et al.  Over the top video: the gorilla in cellular networks , 2011, IMC '11.

[23]  C-ran the Road towards Green Ran , 2022 .

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

[25]  André Hardy,et al.  An examination of procedures for determining the number of clusters in a data set , 1994 .

[26]  Minas Gjoka,et al.  On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology , 2015, MobiHoc.