Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
The Precision and Energetic Cost of Snapshot Estimates in Wireless Sensor Networks
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Model-Driven dynamic control of embedded wireless sensor networks
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Feature Clustering for Data Steering in Dynamic Data Driven Application Systems
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
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Wireless sensor networks can be viewed as the integration of three subsystems: a low-impact in situdata acquisition and collection system, a system for inference of process models from observed data and a prioriinformation, 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.