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Approximate Data Collection in Sensor Networks using Probabilistic Models

12 years 1 months ago
Approximate Data Collection in Sensor Networks using Probabilistic Models
Wireless sensor networks are proving to be useful in a variety of settings. A core challenge in these networks is to minimize energy consumption. Prior database research has proposed to achieve this by pushing data-reducing operators like aggregation and selection down into the network. This approach has proven unpopular with early adopters of sensor network technology, who typically want to extract complete "dumps" of the sensor readings, i.e., to run "SELECT *" queries. Unfortunately, because these queries do no data reduction, they consume significant energy in current sensornet query processors. In this paper we attack the "SELECT *" problem for sensor networks. We propose a robust approximate technique called Ken that uses replicated dynamic probabilistic models to minimize communication from sensor nodes to the network's PC base station. In addition to data collection, we show that Ken is well suited to anomaly- and event-detection applications...
David Chu, Amol Deshpande, Joseph M. Hellerstein,
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2006
Where ICDE
Authors David Chu, Amol Deshpande, Joseph M. Hellerstein, Wei Hong
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