- Model-Driven Data Acquisition in Sensor Networks1

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This chapter presents an interactive sensor querying with statistical modeling techniques. Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensomet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. The chapter analyzes that such models can help provide solutions that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. The chapter describes an exponential time algorithm for finding the optimal solution to the optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. It evaluates the approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality. The chapter concludes that this model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains in comparison to traditional data acquisition techniques.

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This work was supported by Intel Corporation, and by NSF under the grant IIS-0205647.

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