Environmental Sensor Networks

Introduction
Environmental sensor networks have the capability of capturing local and broadly-dispersed information simultaneously; they also have the capacity to respond to sudden change in one l ocation by triggering observations selectively across the network while simultaneously updating the underlying complex system model and/or reconfiguring the network. Data gathered by wireless sensor networks, either fixed or mobile, pose unique challenges for environmental modeling: a complex system is being observed by a dynamical network. Technical challenges in statistics (sampling design to prediction and prediction uncertainty), in mathematics (computational geometry to data fusion to robotics), and in computers science (self-organizing networks to algorithm analysis) combine with the technical challenges of the models themselves and the sciences that underlie them.

Sampling from wireless networks:Cost of spatio-temporal data in terms of both energy and delay: Each sample has a footprint in power in space and time, some value to one or more process models (e.g., importance of parameters in space and time, sensitivity of estimates to the observation), and some cost (e.g., data transmission). Is it possible to derive frameworks such that the utility of each sample exceeds its cost?
Frameworks for adaptive sampling: game-theoretic, reinforced learning, dynamic experimental design. How can a sampling scheme to respond to highly non-stationary dynamics in energy-constrained sampling networks. Are modes of operation controlled by adaptive state machines enough?
Environmental modeling from sensor networks:Model complexity and adequacy: In the trade-off of dimensionality and predictive accuracy, what are the diagnostics for excessive vs. insufficient parametrization? Can models be developed so that reduced forms (e.g., deleting submodels, subsets of parameters, or reducing resolution of observations and/or parameter specification) still function simultaneously with near-optimality at several scales?
Prediction Uncertainty: Appropriate modeling of sources of uncertainty due to sampling (e.g., "lost" samples, outliers, bad sensors, measurement noise, and unreliable communication), and integration of data models with process models.
Model adequacy: Is there coherent noise in bio-micrometeorological systems that should drive exploration of new regions of the state space?
Networks, forests and global change:Process level understanding of how forested ecosystems respond to global change is critical for anticipating consequences of human impacts on landscapes. To be successful an approach will entail integrated models of a complex system and will involve heterogeneous data and analyses that directly address uncertainty and model selection issues.
Specific analyses will focus on increasing the capacity to forecast consequences of global change, using existing data from forest sensor networks as both testbed and primary research objective.
Inference on scaling relationships will be implemented in simulation of whole forests to examine potential consequences of changing climate and atmospheric CO2 for forest diversity and carbon sequestration. These results will have immediate application to the problem of forecasting biosphere responses to atmospheric change.