Scholars@Duke publication: From data reverence to data relevance: Model-mediated wireless sensing of the physical environment

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

Publication , Conference

Flikkema, PG; Agarwal, PK; Clark, JS; Ellis, C; Gelfand, A; Munagala, K; Yang, J

Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

January 1, 2007

Published version (DOI)

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. © Springer-Verlag Berlin Heidelberg 2007.

Duke Scholars

Author James S. Clark Environmental Sciences and Policy

Author Alan E. Gelfand Statistical Science

Author Kamesh Munagala Computer Science

Author Jun Yang Computer Science

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/978-3-540-72584-8_130

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540725831

Publication Date

January 1, 2007

Volume

4487 LNCS

Start / End Page

988 / 994

Related Subject Headings

Artificial Intelligence & Image Processing
46 Information and computing sciences

Citation

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MLA

NLM

Flikkema, P. G., Agarwal, P. K., Clark, J. S., Ellis, C., Gelfand, A., Munagala, K., & Yang, J. (2007). From data reverence to data relevance: Model-mediated wireless sensing of the physical environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4487 LNCS, pp. 988–994). https://doi.org/10.1007/978-3-540-72584-8_130

Flikkema, P. G., P. K. Agarwal, J. S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang. “From data reverence to data relevance: Model-mediated wireless sensing of the physical environment.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4487 LNCS:988–94, 2007. https://doi.org/10.1007/978-3-540-72584-8_130.

Flikkema PG, Agarwal PK, Clark JS, Ellis C, Gelfand A, Munagala K, et al. From data reverence to data relevance: Model-mediated wireless sensing of the physical environment. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007. p. 988–94.

Flikkema, P. G., et al. “From data reverence to data relevance: Model-mediated wireless sensing of the physical environment.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4487 LNCS, 2007, pp. 988–94. Scopus, doi:10.1007/978-3-540-72584-8_130.

Flikkema PG, Agarwal PK, Clark JS, Ellis C, Gelfand A, Munagala K, Yang J. From data reverence to data relevance: Model-mediated wireless sensing of the physical environment. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007. p. 988–994.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

10.1007/978-3-540-72584-8_130

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540725831

Publication Date

January 1, 2007

Volume

4487 LNCS

Start / End Page

988 / 994

Related Subject Headings

Artificial Intelligence & Image Processing
46 Information and computing sciences