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

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.