An overview of mobile device network traffic and network interface usage patterns

Junghyo Lee, Patrick Seeling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

In this paper, we present and evaluate the outcomes of a measurement study amongst Android mobile device users, who volunteered their device-level network activity data through a newly developed mobile application in January 2013. We evaluate the submitted data in two hour time intervals with respect to device-level network traffic amounts, application network activity times, and data distribution (as measure of connectivity) between mobile (cellular) and wireless LAN networks. We find fairly homogeneous values with low levels of autocorrelation or long range dependence for the device-level amounts of data, but an indication for self-similarity for the summed application network usage times, which are positively correlated. In addition, we observe that the average distribution for cellular interface usage exhibits clear patterns for the day of the week as well as the time of the day. The combination of these findings can find direct utilization in future mobile device utilization modeling efforts.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Electro/Information Technology, EIT 2013
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Electro/Information Technology, EIT 2013 - Rapid City, SD, United States
Duration: May 9 2013May 11 2013

Publication series

NameIEEE International Conference on Electro Information Technology
ISSN (Print)2154-0357
ISSN (Electronic)2154-0373

Conference

Conference2013 IEEE International Conference on Electro/Information Technology, EIT 2013
Country/TerritoryUnited States
CityRapid City, SD
Period05/9/1305/11/13

Keywords

  • Android
  • Mobile applications
  • connectivity
  • network traffic

Fingerprint

Dive into the research topics of 'An overview of mobile device network traffic and network interface usage patterns'. Together they form a unique fingerprint.

Cite this