Using artificial intelligence to optimize wireless sensor network deployments for sub-alpine biogeochemical process studies

Lynette L. Laffea, Russ Monson, Richard Han, John K. Williams

Research output: Contribution to conferencePaper

3 Scopus citations

Abstract

Techniques for placing wireless sensors to adequately capture measurements of interest and dynamically managing their reporting accuracy and network topology to capture significant events while maximizing battery life will become more important as wireless sensor networks continue to enter complex new areas of application, such as the Niwot ridge deployment described in the present paper. Previous sensor array research in this area has focused primarily on theoretical analysis independent of actual network operation and physical process evolution. We believe the artificial intelligence techniques we have described will offer insight into whole system management and process system discovery, while paving the way for a complex sensor deployment that we hope will cast new light on the biogeochemical processes related to global warming.

Original languageEnglish (US)
StatePublished - Dec 1 2007
Externally publishedYes
Event87th AMS Annual Meeting - San Antonio, TX, United States
Duration: Jan 14 2007Jan 18 2007

Other

Other87th AMS Annual Meeting
CountryUnited States
CitySan Antonio, TX
Period1/14/071/18/07

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ASJC Scopus subject areas

  • Environmental Engineering
  • Global and Planetary Change
  • Management, Monitoring, Policy and Law

Cite this

Laffea, L. L., Monson, R., Han, R., & Williams, J. K. (2007). Using artificial intelligence to optimize wireless sensor network deployments for sub-alpine biogeochemical process studies. Paper presented at 87th AMS Annual Meeting, San Antonio, TX, United States.