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Optimizing Energy-Efficient Query
Processing in Wireless Sensor
Networks
Ross Rosemark Wang-Chien Lee Bhuvan Urgaonkar
Department of Computer Science and Engineering
The Pennsylvania State University
任課老師：陳朝鈞
報告者：邱志銘、黃烱育
2016/7/13
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Outline
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Introduction
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Query Optimization
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Performance Evaluation
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Conclusion
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Introduction
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Energy-efficient query processing for
wireless sensor networks (WSNs)
Choosing an efficient query plan
Two primary contribute to the energy
consumption of WSNs
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Sensing cost
Communication cost
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Introduction
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Existing query optimizers for WSNs
typically
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Sensing cost
In this paper
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Query execution
Including routing
Sensing
data/metadata collection
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Introduction
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data/metadata
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In query optimizers for database management
systems
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Query optimization
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Remove the wireless sensor network data
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Query Optimization
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Provides a highlevel overview of our query
optimizer.
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Classification Stage
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Refinement Stage
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Query Optimization
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Preferences
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Q：a single query
p：predicates
n：node
s：sensor
m：total number of query plans generated
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Query Optimization
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Classification Stage
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Determine if metadata should be collected by
evaluating each query plan within .
Based on , the query optimizer chooses
QPmin
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no metadata collection
metadata collection
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Query Optimization
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No metadata collection
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Finding a better query plan.
Metadata collection
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Query optimizer enters the second stage
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Query Optimization
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Refinement Stage
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Collect metadata to re-evaluate each query plan
in
distributed to each node
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choosing
Routes back the desired tuples to the AP
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PERFORMANCE EVALUATION
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Impact its total energy consumption
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The number of reportings
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The unit energy cost of each sensor sampling
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PERFORMANCE EVALUATION
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Number of Reports
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PERFORMANCE EVALUATION
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Unit Cost of Sensor Sampling
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PERFORMANCE EVALUATION
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Effectiveness of Query Optimizer
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query optimizer chooses most energy-efficient
query
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evaluate over a thousand query plans
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PERFORMANCE EVALUATION
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Light Query
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PERFORMANCE EVALUATION
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Heavy Query
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PERFORMANCE EVALUATION
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Randomly Generated Queries
Optimization on Communication Cost
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CONCLUSION
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In this paper they examine the design of a query
optimizer for WSNs.
The approach optimizes on routing、sensing and
data/metadata collection.
The approach can more accurately estimated
query plans in potential gain.
In the future they plan to extend our approach to
support multiple queries simultaneously.
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