NSDI '06 Abstract
Pp. 311324 of the Proceedings
PRESTO: Feedback-driven Data Management in Sensor Networks
Ming Li, Deepak Ganesan, and Prashant Shenoy,
University of Massachusetts, Amherst
This paper presents PRESTO, a novel two-tier sensor data management
architecture comprising proxies and sensors that cooperate with one
another for acquiring data and processing queries. PRESTO proxies
construct time-series models of observed trends in the sensor data and
transmit the parameters of the model to sensors. Sensors check sensed
data with model-predicted values and transmit only deviations from the
predictions back to the proxy. Such a model-driven push approach is
energy-efficient, while ensuring that anomalous data trends are never
missed. In addition to supporting queries on current data, PRESTO also
supports queries on historical data using interpolation and local
archival at sensors. PRESTO can adapt model and system parameters to
data and query dynamics to further extract energy savings. We have
implemented PRESTO on a sensor testbed comprising Intel Stargates and
Telos Motes. Our experiments show that in a temperature monitoring
application, PRESTO yields one to two orders of magnitude reduction in
energy requirements over on-demand, proactive or model-driven pull
approaches. PRESTO also results in an order of magnitude reduction in
query latency in a 1% duty-cycled five hop sensor network over a
system that forwards all queries to remote sensor nodes.
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