Characterizing the utility of smartphone background traffic

The incredible rise in popularity of mobile smart devices has placed tremendous pressure on wireless service providers. While much of the pressure arises from increasingly rich multimedia and social offerings, a sizable portion of the traffic originates when the user is not actively interacting with the device. The focus of this paper is to explore the prevalence and utility of smartphone background traffic through a pool of over one hundred campus smartphone users over a seven-week period from the Spring of 2013. Notably, our work shows that background traffic constitutes a non-trivial portion of wireless traffic ranging between one-third to two-fifths of traffic across the wireless interfaces. Our work breaks down background traffic with respect to diurnal behavior, wireless interface, mobile application, and latency until screen activation to further characterize the data.

[1]  Peng Zhou,et al.  Background Traffic Analysis for Social Media Applications on Smartphones , 2012, 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control.

[2]  Wei Pan,et al.  SoundSense: scalable sound sensing for people-centric applications on mobile phones , 2009, MobiSys '09.

[3]  Cecilia Mascolo,et al.  EmotionSense: a mobile phones based adaptive platform for experimental social psychology research , 2010, UbiComp.

[4]  Ranveer Chandra,et al.  Optimizing background email sync on smartphones , 2013, MobiSys '13.

[5]  Jong Min Lee,et al.  Battery life time extension method using selective data reception on smartphone , 2012, The International Conference on Information Network 2012.

[6]  Imad Aad,et al.  The Mobile Data Challenge: Big Data for Mobile Computing Research , 2012 .

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

[8]  Ning Ding,et al.  Characterizing and modeling the impact of wireless signal strength on smartphone battery drain , 2013, SIGMETRICS '13.

[9]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

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

[11]  Christian Poellabauer,et al.  Lessons learned from the netsense smartphone study , 2013, HotPlanet '13.

[12]  David Kotz,et al.  Analysis of a Campus-Wide Wireless Network , 2002, MobiCom '02.

[13]  Christophe Diot,et al.  Impact of Human Mobility on Opportunistic Forwarding Algorithms , 2007, IEEE Transactions on Mobile Computing.

[14]  Geoffrey M. Voelker,et al.  Access and mobility of wireless PDA users , 2003, MOCO.

[15]  D. Lazer,et al.  Inferring Social Network Structure using Mobile Phone Data , 2006 .