Advertisement
Published in:
2011 | OriginalPaper | Chapter
Modeling and Discovering Occupancy Patterns in Sensor Networks Using Latent Dirichlet Allocation
Authors : Federico Castanedo, Hamid Aghajan, Richard Kleihorst
Published in: Foundations on Natural and Artificial Computation
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.