Abstract
This book chapter is a review of mobile sensing technologies and computational methods for collective intelligence. We discuss the application of mobile sensing to understand collective mechanisms and phenomena in face-to-face networks at three different scales: organizations, communities and societies. We present an overview of the state-of-the art in individual behavior recognition from sensor data. We discuss related work on group behavior recognition such as face-to-face interaction, social signaling, conversation detection, and conversation dynamics. We also present a brief overview of pattern recognition methods in social network analysis for the automatic identification of groups and the study of social network evolution. We describe a sensor-based organizational design and engineering system for computational collective intelligence applications in organizations. We also provide two example applications of collective intelligence and modeling user behavior at the community scale. Finally, we investigate the impact that these new sensing technologies may have on the understanding of societies, and how these insights can assist in the design of smarter cities and countries.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abdelzaher, T., Anokwa, Y., Boda, P., et al.: Mobiscopes for Human Spaces. IEEE Pervasive Computing, 20–29 (2007)
Avancha, S., Baxi, A., Kotz, D.: Privacy in mobile technology for personal healthcare. ACM Computing Surveys (2009)
Aaron, C., Moore, C., Newman, M.: Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98–101 (2008)
Arbesman, S., Kleinberg, J., Strogatz, S.: Superlinear scaling for innovation in cities. Physical Review E 79(1), 16115 (2009)
Bagrow, J.P., Bollt, E.M.: Local method for detecting communities. Phys. Rev. E. 046108 (2005)
Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Proceedings of the 2nd International Conference on Pervasive Computing, pp. 1–17 (2004)
Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C., West, G.: In: Proceedings of the National Academy of Sciences, vol. 104, p. 7301 (2007)
Bernard, H.R., Killworth, P., Kronenfeld, D., Sailer, L.: The Problem of Informant Accuracy: The Validity of Retrospective Data. Annual Review of Anthropology 13, 495–517 (1984)
Cebrian, M., Lahiri, M., Oliver, N., Pentland, A.: Measuring the Collective Potential of Populations From Dynamic Social Interaction Data. IEEE Journal of Selected Topics in Signal Processing 4(4), 667–686 (2010)
Lahiri, M., Cebrian, M.: The genetic algorithm as a general diffusion model for social networks. In: Proceedings of the AAAI Conference on Artificial Intelligence (2010)
Choudhury, T., Pentland, A.: Sensing and Modeling Human Networks Using the Sociometer. In: 7th International Symposium on Wearable Computers, October 21-23 (2003)
Chung, P.-C., Liua, C.-D.: A daily behavior enabled hidden Markov model for human behavior understanding. Pattern Recognition, 1572–1580 (2007)
Ciccone, A., Hall, R.: Productivity and the density of economic activity. The American Economic Review 86, 54–70 (1996)
Ciccone, A.: Agglomeration effects in Europe. European Economic Review 46(2), 213–227 (2002)
DeVaul, R.W., Dunn, S.: Real-Time Motion Classification for Wearable Computing Applications. Technical Report. MIT Media Laboratory (2001)
Dong, W., Pentland, A.: Modeling Influence Between Experts. In: Huang, T.S., Nijholt, A., Pantic, M., Pentland, A. (eds.) ICMI/IJCAI Workshops 2007. LNCS (LNAI), vol. 4451, pp. 170–189. Springer, Heidelberg (2007)
Eagle, N., Pentland, A., Lazer, D.: Inferring Social Network Structure Using Mobile Phone Data. In: Proceedings of NAS, vol. 106, pp. 15274–15278 (2009)
Gonzalez, M., Hidalgo, C., Barabasi, A.-L.: Understanding Human Mobility Patterns. Nature 453, 779–982 (2008)
Foltz, P.W., Martin, M.J.: Automated Communication Analysis of Teams. In: Salas, E., Goodwin, G.F., Burke, S. (eds.) Team Effectiveness in Complex Organizations, pp. 411–431. Taylor & Francis Group, New York (2009)
Gatica-Perez, D.: Analyzing group interactions in conversations: a review. In: Proceedings of the International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 41–46. IEEE, Los Alamitos (2006)
Gatica-Perez, D.: Automatic nonverbal analysis of social interaction in small groups: A review. Image and Vision Computing (2009)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. In: PNAS, pp. 7821–7826 (2002)
Glaeser, E., Kallal, H., Scheinkman, J., Shleifer, A.: Growth in Cities. Journal of Political Economy 100, 1126–1152 (1992)
Goldenberg, A.: Scalable Graphical Models for Social Networks. PhD Thesis. Pittsburgh, PA, USA: Carnegie Mellon University (2007)
Granovetter, M.: The strength of weak ties. American Journal of Sociology 78, 1360–1380 (1973)
Handcock, M., Raftery, A., Tantrum, J.: Model-based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society) 170(2), 301–354 (2007)
Heylighen, F.: Collective Intelligence and its Implementation on the Web: algorithms to develop a collective mental map. Computational and Mathematical Organization Theory 5(3), 253–280 (1999)
Hinds, P.: Distributed Work. MIT Press, Cambridge (2002)
Jacobs, J.: The economy of cities. Vintage Books (1969)
Jacobs, J.: Cities and the wealth of nations: Principles of economic life. Random House (1984)
Kern, N., Schiele, B.: Context-Aware Notification for Wearable Computing. In: Proceedings of the 7th International Symposium on Wearable Computing, pp. 223–230 (2003)
Kossinets, G., Watts, D.J.: Empirical Analysis of Evolving Social Networks. Science, 88–90 (2006)
Bramoullé, Y., Kranton, R.: Public Goods in Networks. Journal of Economic Theory 135(1), 478–494 (2007)
Lahiri, M., Cebrian, M.: The genetic algorithm as a general diffusion model for social networks. In: Proc. of the 24th AAAI Conference on Artificial Intelligence, Atlanta, Georgia (2010)
Lazer, D., Friedman, A.: The Network Structure of Exploration and Exploitation. Administrative Science Quarterly 52(4), 667–694 (2007)
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., et al.: Computational Social Science. Science 323, 721–723 (2009)
Lee, S.W., Mase, K.: Activity and Location Recognition Using Wearable Sensors. In: Pervasive Computing, pp. 24–32 (2002)
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, p. 187 (2005)
Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing. ACM Transactions on the Web, 1–1 (2007)
Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)
Liao, L., Choudhury, T., Fox, D., Kautz, H.: Training Conditional Random Fields Using Virtual Evidence Boosting. In: Proceedings of the International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 1–6 (2007)
Macy, M., Eagle, N., Claxton, R.: Network Diversity and Economic Development. Science 328(5981), 1029 (2010)
Madan, A., Pentland, A.: Modeling Social Diffusion Phenomena Using Reality Mining. In: AAAI Spring Symposium on Human Behavior Modeling, Stanford University, CA (2009)
Madan, A., Pentland, A.: Social Sensing of Political Opinions (2010) (in submission)
McCallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email. Journal of Artificial Intelligence Research 30, 249–272 (2007)
Mantyjarvi, J., Himberg, J., Seppanen, T., Center, N.: Recognizing human motion with multiple acceleration sensors. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 747–752 (2001)
Mishra, N., Schreiber, R., Stanton, I., Tarjan, R.E.: Clustering social networks. In: Bonato, A., Chung, F.R.K. (eds.) WAW 2007. LNCS, vol. 4863, pp. 56–67. Springer, Heidelberg (2007)
Olguin-Olguin, D.: Sociometric Badges: Wearable Technology for Measuring Human Behavior. Master’s Thesis. Cambridge, MA, USA: Massachusetts Institute of Technology (2007)
Olguin-Olguin, D., Gloor, P.A., Pentland, A.: Wearable Sensors for Pervasive Healthcare Management. In: 3rd International Conference on Pervasive Computing Technologies for Healthcare, London, UK, April 1-3, pp. 1–4 (2009a)
Olguin-Olguin, D., Waber, B., Kim, T., Mohan, A., Ara, K., Pentland, A.: Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 39(1), 43–55 (2009b)
Olguin-Olguin, D., Pentland, A.: Sensor-Based Organisational Design and Engineering. To appear in: International Journal of Organisational Design and Engineering (2010)
Oliver, N., Horvitz, E.: A Comparison of HMMs and Dynamic Bayesian Networks for Recognizing Office Activities. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 199–209. Springer, Heidelberg (2005)
Oliver, N., Garg, A., Horvitz, E.: Layered representations for learning and inferring office activity from multiple sensory channels. Computer Vision and Image Segmentation 96, 163–180 (2004)
Palla, G., Barabasi, A.L., Vicsek, T.: Quantifying social group evolution. Nature, 664–667 (2007)
Pentland, A.: Automatic Mapping and Modeling of Human Networks. Physica A: Statistical Mechanics and its Applications 378(1), 59–67 (2006)
Pentland, A.: Honest Signals: How they Shape our World. The MIT Press, Cambridge (2008)
Ravasz, E., Barabasi, A.: Physical Review E 67, 26112 (2003)
Robins, G., Snijders, T., Wang, P., Handcock, M., Pattison, P.: Recent developments in exponential random graph (p*) models for social networks. Social Networks 29(2), 192–215 (2007)
Salganik, M., Dodds, P., Watts, D.: Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. Science 311 (2006)
Scarlat, E., Maries, I.: Towards an Increase of Collective Intelligence within Organizations Using Trust and Reputation Models. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 140–151. Springer, Heidelberg (2009)
Slaughter, A.J., Yu, J., Koehly, L.M.: Social Network Analysis: Understanding the Role of Context in Small Groups and Organizations. In: Salas, E., Goodwin, G.F., Burke, S. (eds.) Team Effectiveness in Complex Organizations, pp. 433–459. Taylor & Francis Group LLC, Abington (2009)
Sukthankar, G., Sycara, K.: A cost minimization approach to human behavior recognition. In: Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, The Netherlands, pp. 1067–1074 (2005)
Van Laerhoven, K., Cakmakci, O.: What shall we teach our pants? In: Proceedings of the 4th International Symposium on Wearable Computers, pp. 77–83 (2000)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, London (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Olguín, D.O., Madan, A., Cebrian, M., Pentland, A.(. (2011). Mobile Sensing Technologies and Computational Methods for Collective Intelligence. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-20344-2_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20343-5
Online ISBN: 978-3-642-20344-2
eBook Packages: EngineeringEngineering (R0)