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 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.

[1]  Scott E. Hudson,et al.  Using visualizations to increase compliance in experience sampling , 2008, UbiComp.

[2]  Alex Pentland,et al.  Social serendipity: mobilizing social software , 2005, IEEE Pervasive Computing.

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

[4]  Fehmi Ben Abdesslem,et al.  Reliable Online Social Network Data Collection , 2012, Computational Social Networks.

[5]  Nicholas A. Christakis,et al.  Social contagion theory: examining dynamic social networks and human behavior , 2011, Statistics in medicine.

[6]  Aaron Striegel,et al.  Accurate Extraction of Face-to-Face Proximity Using Smartphones and Bluetooth , 2011, 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN).

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