Model-Driven Data Acquisition in Sensor Networks

Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet 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. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe a...

• Type: Conference
• Year: 2004
• Where: VLDB
• Authors: Amol Deshpande, Carlos Guestrin, Samuel Madden, Joseph M. Hellerstein, Wei Hong
• Keywords: Data Acquisition, Database, Sensor, Sensor Readings, VLDB 2004