Probabilistic Processing of Interval-valued Sensor Data

S. Evers, M.M. Fokkinga, Peter M.G. Apers

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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.
Original languageUndefined
Title of host publicationProceedings of the 5th International Workshop on Data Management for Sensor Networks (DMSN2008)
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages42-48
Number of pages7
ISBN (Print)978-1-60558-284-9
DOIs
Publication statusPublished - 24 Aug 2008

Publication series

NameACM International Conference Proceeding Series
PublisherACM
NumberDTR08-9

Keywords

  • IR-64883
  • EWI-13072
  • METIS-251087
  • DB-DMSN: Data Management for Sensor Networks
  • CR-H.2.8

Cite this

Evers, S., Fokkinga, M. M., & Apers, P. M. G. (2008). Probabilistic Processing of Interval-valued Sensor Data. In Proceedings of the 5th International Workshop on Data Management for Sensor Networks (DMSN2008) (pp. 42-48). [10.1145/1402050.1402060] (ACM International Conference Proceeding Series; No. DTR08-9). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/1402050.1402060