Modeling and Discovering Occupancy Patterns in Sensor Networks Using Latent Dirichlet Allocation - Foundations on Natural and Artificial Computation - page 480

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

Federico Castanedo 1 ,HamidAghajan 2 , and Richard Kleihorst 3

1 Computer Science Department. University Carlos III of Madrid
[e-mail]
2 Department of Electrical Engineering. Stanford University
[e-mail]
3 Vito & Ghent University
[e-mail]

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 oce building. LDA is a generative and
unsupervised probabilistic model for collections of discrete data. Con-
tinuous 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.

Keywords: Probabilistic modeling, Sensor networks, Latent topics.

1 Introduction

The main objective of this paper is to show a way to perform probabilistic mod-
eling of occupancy patterns from a sensor network without having any apriori or
ground-truth information about the behaviors, thus following an unsupervised
technique.
More specifically, this paper presents an approach based on the Latent Dirich-
let Allocation (LDA) model [1] for modeling and discovering occupancy patterns
in an oce environment using a sensor network. LDA is a generative and un-
supervised machine learning probabilistic model for collections of discrete data.
In a generative machine learning algorithm, the model adjust the parameters to
produce the underlying data, so the model fits to the provided data. The LDA
model is one of the hierarchical Bayesian text models that has been proposed
in the research community. It overcomes some of the limitations that have been
reported with the probabilistic Latent Semantic Indexing (pLSI) [2], such as the
over-fitting problems. Since Blei's original paper [1], LDA has been successfully