Query Processing in Sensor Networks

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Query Processing in Sensor Networks
11/09/2006
Se Ho Park Hyun Ik Jang Frolin Rusu
W. Hong S. Madden Implementation and Research
Issues in Query Processing for Wireless Sensor
Networks, ICDE 2004
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Outline
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Outline
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Query Processor
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TinyDB
Root (gateway node)
Distributed in Network Query processor
Wireless Sensor networks
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Client PC-Base station
Query parsing Query optimization
Query result storage and display
Data
Query
Routing Tree
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Outline
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SELECT ltaggregatesgt, ltattributesgt FROM sensors
ltbuffergt WHERE ltpredicatesgt GROUP BY
ltexprgt HAVING ltpredicatesgt SAMPLE PERIOD
ltconstgt ONCE INTO ltbuffergt
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• Single table in FROM clause
• No sub-queries
• No column alias in SELECT clause
• Only fundamental difference SAMPLE PERIOD clause
• The result of query is a stream of values (rather
  than single aggregate value)

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TinySQL Example
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Event-based Queries

• ON event SELECT
• Run query only when interesting events happen
• Event examples
• Button pushed
• Message arrival
• Bird enters nest
• Analogous to triggers but events are user-defined

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Query over Stored Data

• Named buffers in Flash memory
• Store query results in buffers
• Query over named buffers
• Analogous to materialized views
• Example
• CREATE BUFFER name SIZE x (field1 type1, field2
  type2, )
• SELECT a1, a2 FROM sensors SAMPLE PERIOD d INTO
  name
• SELECT field1, field2, FROM name SAMPLE PERIOD d

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Outline
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Tree-based Routing

• Tree-based routing
• Used in
• Query delivery
• Data collection
• In-network aggregation

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Basic Aggregation

• In each epoch
• Each node samples local sensors once
• Generates partial state record (PSR)
• local readings
• readings from children
• Outputs PSR during assigned comm. interval
• At end of epoch, PSR for whole network output at
  root
• New result on each successive epoch

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Illustration Aggregation
Interval 4
Interval
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Illustration Aggregation
Interval 3
Interval
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Illustration Aggregation
Interval 2
Interval
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Illustration Aggregation
Interval 1
Interval
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Illustration Aggregation
Interval 4
Interval
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Outline
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Inside TinyDB
Multihop Network
Query Processor
Schema
TinyOS
TinyDB
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Acquisitional Query Processing (ACQP)

• TinyDB acquires AND processes data
• Could generate an infinite number of samples
• An acqusitional query processor controls
• when,
• where,
• and with what frequency data is collected!
• Versus traditional systems where data is provided
  a priori

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ACQP Whats Different?

• How should the query be processed?
• Sampling as a first class operation
• How does the user control acquisition?
• Rates or lifetimes
• Event-based triggers
• Which nodes have relevant data?
• Index-like data structures
• Which samples should be transmitted?
• Prioritization, summary, and rate control

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Operator Ordering Interleave Sampling Selection

• E(sampling mag) gtgt E(sampling light)
• 1500 uJ vs. 90 uJ

• SELECT light, mag
• FROM sensors
• WHERE pred1(mag)
• AND pred2(light)
• SAMPLE PERIOD 1s

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Conclusions

• Architecture
• Data Model and Query Language
• Query Dissemination and Result Collection
• Query Processing

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