Modeling and Discovering Occupancy Patterns in Sensor Networks Using Latent Dirichlet Allocation

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.