@inproceedings{atzmueller2013towards,
  address = {New York, NY, USA},
  author = {Atzmueller, Martin and Hilgenberg, Katy},
  booktitle = {Proc. 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013},
  interhash = {b0d93d41ff9e84514d614cd2b3507a1d},
  intrahash = {4ebea4979524a9c1c0d41845e41e33a9},
  publisher = {ACM Press},
  title = {{Towards Capturing Social Interactions with SDCF: An Extensible Framework for Mobile Sensing and Ubiquitous Data Collection}},
  year = 2013
}

@inproceedings{atzmueller2013sensor,
  address = {Hamburg, Germany},
  author = {Atzmueller, Martin and Hilgenberg, Katy},
  booktitle = {Proc. Sunbelt XXXIII: Annual Meeting of the International Network for Social Network Analysis},
  interhash = {b71797fb6ff8776761d5227a61875470},
  intrahash = {5f8a4602c1087ea93f1f7440050d1982},
  publisher = {INSNA},
  title = {{SDCF - A Sensor Data Collection Framework for Social and Ubiquitous Environments: Challenges and First Experiences in Sensor-based Social Networks (Abstract)}},
  year = 2013
}

@article{6069707,
  abstract = {An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy.},
  author = {Ganti, R.K. and Ye, Fan and Lei, Hui},
  doi = {10.1109/MCOM.2011.6069707},
  interhash = {8e5a7301965e4703f5caaf45f7a823d4},
  intrahash = {44321e103e910e997453ffcb9877ac73},
  issn = {0163-6804},
  journal = {Communications Magazine, IEEE},
  number = 11,
  pages = {32-39},
  title = {Mobile crowdsensing: current state and future challenges},
  url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6069707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6069707},
  volume = 49,
  year = 2011
}

@article{song2010limits,
  abstract = {A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
},
  author = {Song, Chaoming and Qu, Zehui and Blumm, Nicholas and Barabási, Albert-László},
  doi = {10.1126/science.1177170},
  eprint = {http://www.sciencemag.org/cgi/reprint/327/5968/1018.pdf},
  interhash = {f2611a08bf6db54f86e884c05f3cb5fb},
  intrahash = {a89330f8eb32ce62b5f5c9a2b4909f25},
  journal = {Science},
  number = 5968,
  pages = {1018--1021},
  title = {Limits of Predictability in Human Mobility},
  url = {http://www.barabasilab.com/pubs/CCNR-ALB_Publications/201002-19_Science-Predictability/201002-19_Science-Predictability.pdf},
  volume = 327,
  year = 2010
}