In-network data estimation for sensor-driven scientific applications

Nanyan Jiang, Manish Parashar

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

1 Scopus citations


Sensor networks employed by scientific applications often need to support localized collaboration of sensor nodes to perform in-network data processing. This includes new quantitative synthesis and hypothesis testing in near real time, as data streaming from distributed instruments, to transform raw data into high level domain-dependent information. This paper investigates in-network data processing mechanisms with dynamic data requirements in resource constrained heterogeneous sensor networks. Particularly, we explore how the temporal and spatial correlation of sensor measurements can be used to trade off between the complexity of coordination among sensor clusters and the savings that result from having fewer sensors involved in in-network processing, while maintaining an acceptable error threshold. Experimental results show that the proposed in-network mechanisms can facilitate the efficient usage of resources and satisfy data requirement in the presence of dynamics and uncertainty.

Original languageEnglish (US)
Title of host publicationHigh Performance Computing - HiPC 2008 - 15th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Print)354089893X, 9783540898931
StatePublished - 2008
Event15th International Conference on High Performance Computing, HiPC 2008 - Bangalore, India
Duration: Dec 17 2008Dec 20 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5374 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th International Conference on High Performance Computing, HiPC 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • In-network data estimation
  • Sensor system programming
  • Sensor-driven scientific applications


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