Exploiting data correlation for multi-scale processing in sensor networks

Xiaoning Cui, Baohua Zhao, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


With the emergence of large and multi-scale sensor networks, the technologies of multi-scale processing among various sensors become an essential issue. In this paper, the problem of exploiting data correlation for multi-scale sensor networks is considered, and an architecture exploiting correlation is designed for both intraand inter-data processing. Our correlation-adaptive scheme follows the characteristics of real sensor data, and fills the gap of the correlation models addressed by most of previous research with inherent support for related data gathering algorithms. A core solution module of this architecture is devised, and theoretical analysis and simulation studies are conducted on real-world datasets. Through the real-world data experiments in terms of accuracy and energy-consumption evaluation, the correlationadaptive scheme is shown to work well in multi-scale processing sensor networks.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Scalable Information Systems, InfoScale 2007
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781595937575
Publication statusPublished - 6 Jun 2007
Externally publishedYes
Event2nd International Conference on Scalable Information Systems, InfoScale 2007 - Suzhou, China
Duration: 6 Jun 20078 Jun 2007

Publication series

NameACM International Conference Proceeding Series


Conference2nd International Conference on Scalable Information Systems, InfoScale 2007


  • Correlation adjustment function
  • Correlation exploiting architecture
  • Data sample
  • Intra-/inter-data correlation
  • Multi-scale processing
  • User query
  • Wireless sensor network

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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