Probabilistic processing of interval-valued sensor data

Authors:
Sander Evers;Maarten M. Fokkinga;Peter M. G. Apers
Affiliations:
University of Twente, The Netherlands;University of Twente, The Netherlands;University of Twente, The Netherlands
Venue:
Proceedings of the 5th workshop on Data management for sensor networks
Year:
2008

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Abstract

When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two variants on the Hidden Markov Model in which this is possible: a reading extends over an arbitrary number of hidden states. We derive inference algorithms for the models, and analyse their efficiency. For this, we introduce a new method: we start with an inefficient algorithm directly derived from the model, and visually optimize it using a sum-factor diagram.