TinyDB: an acquisitional query processing system for sensor networks

TinyDB: система приобретательного выполнения запросов для сенсорных сетей
Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, Wei Hong
2005-03-01

TinyDBacquisitional query processingdistributed query processorpower consumptionsensor networks
We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired ( sampled ) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.
1
Acquisitional awareness influences SQL extensions, query optimization, data dissemination, and query execution.
2
Considering the locations and costs of data acquisition significantly reduces power consumption compared with traditional passive systems.
3
Evaluation on smart sensor devices shows that acquisitional techniques provide substantial energy savings.
4
The approach explicitly controls where, when, and how often sensor data is sampled and delivered to query operators.
5
TinyDB introduces an acquisitional query-processing system for distributed data collection in sensor networks.

TinyDB distributed query processing system for smart sensor networks

Acquisitional query processing, including the location, timing, frequency, cost, optimization, dissemination, and execution of physical data acquisition to reduce power consumption

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2005-03-01
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Authors
Samuel Madden
Michael J. Franklin
Joseph M. Hellerstein
Wei Hong
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