Scholars@Duke

Model-driven dynamic control of embedded wireless sensor networks

Publication ,  Journal Article
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, 2006
Published version (DOI)

Next-generation wireless sensor networks may revolutionize understanding of environmental change by assimilating heterogeneous data, assessing the relative value and costs of data collection, and scheduling activities accordingly. Thus, they are dynamic, data-driven distributed systems that integrate sensing with modeling and prediction in an adaptive framework. Integration of a range of technologies will allow estimation of the value of future data in terms of its contribution to understanding and cost. This balance is especially important for environmental data, where sampling intervals will range from meters and seconds to landscapes and years. In this paper, we first describe a general framework for dynamic data-driven wireless network control that combines modeling of the sensor network and its embedding environment, both in and out of the network. We then describe a range of challenges that must be addressed, and an integrated suite of solutions for the design of dynamic sensor networks. © Springer-Verlag Berlin Heildelberg 2006.

Duke Scholars

James S. Clark
Author James S. Clark Environmental Sciences and Policy
Alan E. Gelfand
Author Alan E. Gelfand Statistical Science
Kamesh Munagala
Author Kamesh Munagala Computer Science
Jun Yang
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

10.1007/11758532_55

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2006

Volume

3993 LNCS - III

Start / End Page

409 / 416

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Flikkema, P. G., Agarwal, P. K., Clark, J. S., Ellis, C., Gelfand, A., Munagala, K., & Yang, J. (2006). Model-driven dynamic control of embedded wireless sensor networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3993 LNCS-III, 409–416. https://doi.org/10.1007/11758532_55
Flikkema, P. G., P. K. Agarwal, J. S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang. “Model-driven dynamic control of embedded wireless sensor networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3993 LNCS-III (January 1, 2006): 409–16. https://doi.org/10.1007/11758532_55.
Flikkema PG, Agarwal PK, Clark JS, Ellis C, Gelfand A, Munagala K, et al. Model-driven dynamic control of embedded wireless sensor networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006 Jan 1;3993 LNCS-III:409–16.
Flikkema, P. G., et al. “Model-driven dynamic control of embedded wireless sensor networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3993 LNCS-III, Jan. 2006, pp. 409–16. Scopus, doi:10.1007/11758532_55.
Flikkema PG, Agarwal PK, Clark JS, Ellis C, Gelfand A, Munagala K, Yang J. Model-driven dynamic control of embedded wireless sensor networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006 Jan 1;3993 LNCS-III:409–416.

Published In

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

DOI

10.1007/11758532_55

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2006

Volume

3993 LNCS - III

Start / End Page

409 / 416

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences