research-article

Free access

Lessons learned from the netsense smartphone study

Authors:

Aaron Striegel,

Shu Liu,

Lei Meng,

Christian Poellabauer,

David Hachen,

Omar LizardoAuthors Info & Claims

HotPlanet '13: Proceedings of the 5th ACM workshop on HotPlanet

Pages 51 - 56

https://doi.org/10.1145/2491159.2491171

Published: 16 August 2013 Publication History

PDF eReader

Abstract

Over the past few years, smartphones have emerged as one of the most popular mechanisms for accessing content across the Internet driving considerable research to improve wireless performance. A key foundation for such research efforts is the proper understanding of user behavior. However, the gathering of live smartphone data at scale is often difficult and expensive. The focus of this paper is to explore the lessons learned from a two year study of two hundred smart phone users at the University of Notre Dame. In this paper, we offer commentary with regards to the entire process of the study covering aspects including funding considerations, technical architecture design, lessons learned, and recommendations for future efforts gathering live user data.

References

[1]

F. B. Abdesslem, I. Parris, and T. Henderson. Reliable online social network data collection. In Computational Social Networks, pages 183--210, 2012.

Crossref

Google Scholar

[2]

N. Christakis and J. Fowler. Social contagion theory: Examining dynamic social networks and human behavior. In Arxiv Preprint arXiv:1109.5235, 2011.

Google Scholar

[3]

N. Eagle and A. Pentland. Social serendipity: Mobilizing social software. Pervasive Computing, 4(2):28--34, 2005.

Digital Library

Google Scholar

[4]

G. Hsieh, I. Li, A. Dey, J. Forlizzi, and S. E. Hudson. Using visualizations to increase compliance in experience sampling. In Proc. of Ubicomp, pages 164--167, New York, NY, USA, 2008. ACM.

Digital Library

Google Scholar

[5]

J. K. Laurila, D. Gatica-Perez, I. Aad, J. Blom, O. Bornet, T.-M.-T. Do, O. Dousse, J. Eberle, and M. Miettinen. The mobile data challenge: Big data for mobile computing research. In Proc. of Nokia Mobile Data Challenge Workshop, 2012.

Google Scholar

[6]

S. Liu and A. Striegel. Accurate extraction of face-to-face proximity using smartphones and Bluetooth. In Proc. of WiMAN, pages 1--5, 2011.

Crossref

Google Scholar

[7]

A. P. Nathan Eagle and D. Lazer. Inferring social network structure using mobile phone data. Proc. of the National Academy of Sciences (PNAS), 106(36):15274--15278, September 2009.

Google Scholar

Cited By

View all

Hachen DWang CSepulvado BLizardo O(2024)Generators or diffusers? Examining differences in the dynamic coupling of context and social ties across multiple types of fociSocial Networks10.1016/j.socnet.2022.02.00477(151-165)Online publication date: May-2024
https://doi.org/10.1016/j.socnet.2022.02.004
Carley LCarley K(2024)Improved Change Detection in Longitudinal Social Network Measures Subject to Pattern-of-Life VariationsComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_27(328-339)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_27
Wang C(2021)Smoking prevalence, core/periphery network positions, and peer influence: Findings from five datasets on US adolescents and young adultsPLOS ONE10.1371/journal.pone.024899016:3(e0248990)Online publication date: 24-Mar-2021
https://doi.org/10.1371/journal.pone.0248990
Show More Cited By

Index Terms

Lessons learned from the netsense smartphone study
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

Recommendations

Lessons learned from the netsense smartphone study

Over the past few years, smartphones have emerged as one of the most popular mechanisms for accessing content across the Internet driving considerable research to improve wireless performance. A key foundation for such research efforts is the proper ...
Why do smartphone users hesitate to delete unused apps?
MobileHCI '18: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct

The process of installing and removing smartphone apps is simple, but choosing which apps to install or remove requires users' time and effort because users need to categorize and think about which apps they will still use and not use based on their ...
Research on browsing experience of smartphone applications
HCIK '15: Proceedings of HCI Korea

With the expansion of smart-phone market, smart-phones have now become multi-purpose utilitarian tools rather than mere tools of communication. As the number of smart-phone applications increases, users are now provided with more capabilities and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

HotPlanet '13: Proceedings of the 5th ACM workshop on HotPlanet

August 2013

78 pages

ISBN:9781450321778

DOI:10.1145/2491159

General Chair:
Pan Hui
HKUST/T-labs
,
Program Chairs:
Emiliano Miluzzo
AT&T Labs - Research, USA
,
Hamed Haddadi
Queen Mary University of London, UK

ACM SIGCOMM Computer Communication Review Volume 43, Issue 4
October 2013
595 pages
ISSN:0146-4833
DOI:10.1145/2534169
Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGCOMM'13

Sponsor:

SIGCOMM

SIGCOMM'13: ACM SIGCOMM 2013 Conference

August 16, 2013

Hong Kong, China

Acceptance Rates

HotPlanet '13 Paper Acceptance Rate 11 of 20 submissions, 55%;

Overall Acceptance Rate 11 of 20 submissions, 55%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

55
Total Citations
View Citations
557
Total Downloads

Downloads (Last 12 months)80
Downloads (Last 6 weeks)11

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hachen DWang CSepulvado BLizardo O(2024)Generators or diffusers? Examining differences in the dynamic coupling of context and social ties across multiple types of fociSocial Networks10.1016/j.socnet.2022.02.00477(151-165)Online publication date: May-2024
https://doi.org/10.1016/j.socnet.2022.02.004
Carley LCarley K(2024)Improved Change Detection in Longitudinal Social Network Measures Subject to Pattern-of-Life VariationsComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_27(328-339)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_27
Wang C(2021)Smoking prevalence, core/periphery network positions, and peer influence: Findings from five datasets on US adolescents and young adultsPLOS ONE10.1371/journal.pone.024899016:3(e0248990)Online publication date: 24-Mar-2021
https://doi.org/10.1371/journal.pone.0248990
Wang CButts CHipp JLakon C(2020)Model Adequacy Checking/Goodness-of-fit Testing for Behavior in Joint Dynamic Network/Behavior Models, with an Extension to Two-mode NetworksSociological Methods & Research10.1177/004912412091493351:4(1886-1919)Online publication date: 10-Apr-2020
https://doi.org/10.1177/0049124120914933
Purta RStriegel A(2019)Predicting Friendship Pairs from BLE Beacons Using Dining Hall Visits2019 28th International Conference on Computer Communication and Networks (ICCCN)10.1109/ICCCN.2019.8846963(1-9)Online publication date: Jul-2019
https://doi.org/10.1109/ICCCN.2019.8846963
Luceri LCardoso FPapandrea MGiordano SBuwaya JKundig SAngelopoulos CRolim JZhao ZCarrera JBraun TTossou ADimitrakakis CMitrokotsa A(2018)VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of ThingsPervasive and Mobile Computing10.1016/j.pmcj.2018.07.00349(126-138)Online publication date: Sep-2018
https://doi.org/10.1016/j.pmcj.2018.07.003
Bahulkar ASzymanski BChan KLizardo O(2017)Coevolution of a multilayer node-aligned network whose layers represent different social relationsComputational Social Networks10.1186/s40649-017-0047-14:1Online publication date: 6-Nov-2017
https://doi.org/10.1186/s40649-017-0047-1
Sapiezynski PStopczynski AWind DLeskovec JLehmann S(2017)Inferring Person-to-person Proximity Using WiFi SignalsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/30900891:2(1-20)Online publication date: 30-Jun-2017
https://dl.acm.org/doi/10.1145/3090089
Purta RMattingly SSong LLizardo OHachen DPoellabauer CStriegel ABeigl MLukowicz PBlanke UKunze KLee S(2016)Experiences measuring sleep and physical activity patterns across a large college cohort with fitbitsProceedings of the 2016 ACM International Symposium on Wearable Computers10.1145/2971763.2971767(28-35)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2971763.2971767
Choi DShah CSingh VLukowicz PKrüger ABulling ALim YPatel S(2016)Probing the interconnections between geo-exploration and information exploration behaviorProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2971648.2971694(1170-1175)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2971648.2971694
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Lessons learned from the netsense smartphone study

Why do smartphone users hesitate to delete unused apps?

Research on browsing experience of smartphone applications