Multivariate stream data reduction in sensor network applications

Sungbo Seo, Jaewoo Kang, Keun Ho Ryu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


We evaluated several multivariate stream data reduction techniques that can be used in sensor network applications. The evaluated techniques include Wavelet-based methods, sampling, hierarchical clustering, and singular value decomposition (SVD). We tested the reduction methods over the range of different parameters including data reduction rate, data types, number of dimensions and data window size of the input stream. Both real and synthetic time series data were used for the evaluation. The results of experiments suggested that the reduction techniques should be evaluated in the context of applications, as different applications generate different types of data and that has a substantial impact on the performance of different reduction methods. The findings reported in this paper can serve as a useful guideline for sensor network design and construction.

Original languageEnglish
Title of host publicationEmbedded and Ubiquitous Computing - EUC 2005 Workshops
Subtitle of host publicationUISW, NCUS, SecUbiq, USN, and TAUES, Proceedings
Number of pages10
Publication statusPublished - 2005
Externally publishedYes
EventEUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES - Nagasaki, Japan
Duration: 2005 Dec 62005 Dec 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3823 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherEUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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