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Lessons learned from the netsense smartphone study

Published: 16 August 2013 Publication History

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.
[2]
N. Christakis and J. Fowler. Social contagion theory: Examining dynamic social networks and human behavior. In Arxiv Preprint arXiv:1109.5235, 2011.
[3]
N. Eagle and A. Pentland. Social serendipity: Mobilizing social software. Pervasive Computing, 4(2):28--34, 2005.
[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.
[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.
[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.
[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.

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      Published In

      cover image ACM SIGCOMM Computer Communication Review
      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
      • cover image ACM Conferences
        HotPlanet '13: Proceedings of the 5th ACM workshop on HotPlanet
        August 2013
        78 pages
        ISBN:9781450321778
        DOI:10.1145/2491159
      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]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 16 August 2013
      Published in SIGCOMM-CCR Volume 43, Issue 4

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      Author Tags

      1. cellular networks
      2. smartphone
      3. user study
      4. wifi
      5. wireless

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      Cited By

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      • (2023)A Testbed for Automating and Analysing Mobile Devices and Their Applications2023 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC58545.2023.10327947(201-208)Online publication date: 9-Jul-2023
      • (2023)Predicting Relationship Labels and Individual Personality Traits From Telecommunication History in Social Networks Using Hawkes ProcessesIEEE Access10.1109/ACCESS.2023.323897011(8492-8503)Online publication date: 2023
      • (2023)Multidimensional attributes expose Heider balance dynamics to measurementsScientific Reports10.1038/s41598-023-42390-w13:1Online publication date: 20-Sep-2023
      • (2022)Temporal network epistemology: On reaching consensus in a real-world settingChaos: An Interdisciplinary Journal of Nonlinear Science10.1063/5.007499232:6Online publication date: 27-Jun-2022
      • (2022)Generators or diffusers? Examining differences in the dynamic coupling of context and social ties across multiple types of fociSocial Networks10.1016/j.socnet.2022.02.004Online publication date: Feb-2022
      • (2022)Modeling Memory Imprints Induced by Interactions in Social NetworksSocial, Cultural, and Behavioral Modeling10.1007/978-3-031-17114-7_18(186-195)Online publication date: 20-Sep-2022
      • (2021)Social Networks through the Prism of CognitionComplexity10.1155/2021/49639032021Online publication date: 8-Jan-2021
      • (2021)Designing an Interactive Visualization System for Monitoring Participant Compliance in a Large-Scale, Longitudinal StudyExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3443436(1-8)Online publication date: 8-May-2021
      • (2021)Creation, evolution, and dissolution of social groupsScientific Reports10.1038/s41598-021-96805-711:1Online publication date: 1-Sep-2021
      • (2020)Neither influence nor selection: Examining co-evolution of political orientation and social networks in the NetSense and NetHealth studiesPLOS ONE10.1371/journal.pone.023345815:5(e0233458)Online publication date: 29-May-2020
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