Engineering challenges in building sensor networks for real-world applications

Wireless sensor networks can be embedded within complex environments for a wide range of monitoring and control applications including study of fire spread in woods, contamination spread in water bodies, or diffusion of toxic gases through air. In these applications, the collected data is used to drive a forecasting model, typically consisting of a CFD simulation. Traditionally, the data collection process is fixed. Active coupling between the sensor and the model, however, can significantly improve the accuracy, timeliness, and efficiency of forecasting. While there has been significant work in this area, there has not yet been a systematic analysis of how to represent the complex environments, how to represent the model, and how to architect the system. In this paper, we present a generalization of data-coupling applications, and describe a framework for decomposing models, which we apply to a simple example of water contamination.