From data reverence to data relevance: Model-mediated wireless sensing of the physical environment

Paul G. Flikkema, Pankaj K. Agarwal, James S. Clark, Carla Ellis, Alan Gelfand, Kamesh Munagala, Jun Yang

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

2 Scopus citations

Abstract

Wireless sensor networks can be viewed as the integration of three subsystems: a low-impact in situ data acquisition and collection system, a system for inference of process models from observed data and a priori information, and a system that controls the observation and collection. Each of these systems is connected by feedforward and feedback signals from the others; moreover, each subsystem is formed from behavioral components that are distributed among the sensors and out-of-network computational resources. Crucially, the overall performance of the system is constrained by the costs of energy, time, and computational complexity. We are addressing these design issues in the context of monitoring forest environments with the objective of inferring ecosystem process models. We describe here our framework of treating data and models jointly, and its application to soil moisture processes.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I
PublisherSpringer-Verlag
Pages988-994
Number of pages7
ISBN (Print)9783540725831
DOIs
StatePublished - 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: May 27 2007May 30 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4487 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Computational Science, ICCS 2007
Country/TerritoryChina
CityBeijing
Period5/27/075/30/07

Keywords

  • Data relevance
  • Data reverence
  • Wireless sensing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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