@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
}

@article{maisonneuve2010participatory,
  abstract = {Noise pollution is a major problem in cities around the world. The current methods to assess it neglect to represent the real exposure experienced by the citizens themselves, and therefore could lead to wrong conclusions and a biased representations. In this paper we present a novel approach to monitor noise pollution involving the general public. Using their mobile phones as noise sensors, we provide a low cost solution for the citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. Our prototype, called NoiseTube, can be found online [1].},
  author = {Maisonneuve, Nicolas and Stevens, Matthias and Ochab, Bartek},
  doi = {10.3233/IP-2010-0200},
  interhash = {75f1760b3c55de573fffd69fcc10548e},
  intrahash = {4dbb1ce355b7249bc2f66ed4b2126bab},
  journal = {Information Polity},
  month = jan,
  number = 1,
  pages = {51--71},
  publisher = {IOS Press},
  title = {Participatory noise pollution monitoring using mobile phones},
  url = {http://dx.doi.org/10.3233/IP-2010-0200},
  volume = 15,
  year = 2010
}

@article{kanjo2010noisespy,
  abstract = {In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest.},
  acmid = {1831011},
  address = {Hingham, MA, USA},
  author = {Kanjo, Eiman},
  doi = {10.1007/s11036-009-0217-y},
  interhash = {12b29df257d71dfd37193d6b4665004e},
  intrahash = {387ebc6472794f598d07256a45f3d9b7},
  issn = {1383-469X},
  issue_date = {August    2010},
  journal = {Mobile Networks and Applications},
  month = aug,
  number = 4,
  numpages = {13},
  pages = {562--574},
  publisher = {Kluwer Academic Publishers},
  title = {NoiseSPY: A Real-Time Mobile Phone Platform for Urban Noise Monitoring and Mapping},
  url = {http://dx.doi.org/10.1007/s11036-009-0217-y},
  volume = 15,
  year = 2010
}

@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
}

@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.sciencemag.org/cgi/content/abstract/327/5968/1018},
  volume = 327,
  year = 2010
}

@misc{candia-2007,
  abstract = { Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.},
  author = {Candia, J. and Gonzalez, M. C. and Wang, P. and Schoenharl, T. and Madey, G. and Barabasi, A. L.},
  interhash = {16dacb08d5f7c4e2b9876ecba8a99a41},
  intrahash = {ea8b6a4442ccc0cb7dd222f6bd1d992a},
  title = {Uncovering individual and collective human dynamics from mobile phone records},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0710.2939},
  year = 2007
}