Abstract
This paper presents a novel way to perform probabilistic modeling of occupancy patterns from a sensor network. The approach is based on the Latent Dirichlet Allocation (LDA) model. The application of the LDA model is shown using a real dataset of occupancy logs from the sensor network of a modern office building. LDA is a generative and unsupervised probabilistic model for collections of discrete data. Continuous sequences of just binary sensor readings are segmented together in order to build the dataset discrete data (bag-of-words). Then, these bag-of-words are used to train the model with a fixed number of topics, also known as routines. Preliminary obtained results state that the LDA model successfully found latent topics over all rooms and therefore obtain the dominant occupancy patterns or routines on the sensor network.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM, New York (1999)
Griffiths, T., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America 101(suppl. 1), 5228–5235 (2004)
Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79(3), 299–318 (2008)
C. R. Wren, Y. A. Ivanov, D. Leigh, and J. Westhues, “The merl motion detector dataset,” in Proceedings of the 2007 workshop on Massive datasets, ser. MD ’07.plus 0.5em minus 0.4emNew York, NY, USA: ACM, 2007, pp. 10–14. [Online]. Available: http://doi.acm.org/10.1145/1352922.1352926
Connolly, C., Burns, J., Bui, H.: Recovering social networks from massive track datasets. In: IEEE Workshop on Applications of Computer Vision, WACV 2008, pp. 1–8. IEEE, Los Alamitos (2008)
Salah, A., Pauwels, E., Tavenard, R., Gevers, T.: T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data. Sensors 10(8), 7496–7513 (2010)
Magnusson, M.: Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods Instruments and Computers 32(1), 93–110 (2000)
McKeever, S., Ye, J., Coyle, L., Bleakley, C., Dobson, S.: Activity recognition using temporal evidence theory. Journal of Ambient Intelligence and Smart Environments 2(3), 253–269 (2010)
Skogster, P., Uotila, V., Ojala, L.: From mornings to evenings: is there variation in shopping behaviour between different hours of the day? International Journal of Consumer Studies 32(1), 65–74 (2008)
Farrahi, K., Gatica-Perez, D.: Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology 2(1) (2011)
Jordan, M., Ghahramani, Z., Jaakkola, T., Saul, L.: An introduction to variational methods for graphical models. Machine learning 37(2), 183–233 (1999)
Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (2009)
Salton, G., McGill, M.: Introduction to modern information retrieval, New York (1983)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castanedo, F., Aghajan, H., Kleihorst, R. (2011). Modeling and Discovering Occupancy Patterns in Sensor Networks Using Latent Dirichlet Allocation. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-21344-1_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21343-4
Online ISBN: 978-3-642-21344-1
eBook Packages: Computer ScienceComputer Science (R0)