W. Hong & S. Madden – Implementation and Research Issues in Query Processing for Wireless Sensor Networks, ICDE 2004

W. Hong & S. Madden – Implementation and Research Issues in Query Processing for Wireless Sensor Networks, ICDE 2004

OutlineIntroductionI

ArchitectureII

Data Model and Query LanguageIII

Query Dissemination and Result CollectionIV

Query ProcessingV

ConclusionsVI


ArchitectureII



Query ProcessingV

ConclusionsVI

44

Introduction – Sensor Database

SELECT MAX(mag) FROM sensors WHERE mag > threshSAMPLE PERIOD 64ms

• High level abstraction:– Data centric programming– Interact with sensor network

as a whole– Extensible framework

• Under the hood:– Intelligent query processing:

query optimization, power efficient execution

– Fault Mitigation: automatically introduce redundancy, avoid problem areas

App

Sensor Network

Query Processor

Query, Trigger Data

55

Architecture - overall

TinyDB GUI

TinyDB Client API DBMS

Sensor network

TinyDB query

processor

0

4

0

1

5

2

6

3

7

JDBC

Mote side

PC side

8

66

Architecture - mote

TinyDB

Distributed in Network Query processor

Query reception

Query processing

Runtime Adaptation

CatalogTinyOS sampling,

communication

Root (gateway node)

Wireless Sensor networks

77

Architecture - server

Client PC-Base station

DataQuery

Query parsing

Query optimization Query result storage and display

Routing Tree


ArchitectureII



Query ProcessingV

ConclusionsVI

99

Data Model

• Entire sensor network as one single, infinitely-long logical table: sensors

• Columns consist of all the attributes defined in the network

• Typical attributes:– Sensor readings

– Meta-data: node id, location, etc.

– Internal states: routing tree parent, time-stamps, queue length, etc.

• Nodes return NULL for unknown attributes• On server, all attributes are defined in catalog.xml

1010

Query Language - TinySQL

SELECT <aggregates>, <attributes>[FROM {sensors | <buffer>}][WHERE <predicates>][GROUP BY <expr>][HAVING <predicates>][SAMPLE PERIOD <const> | ONCE][INTO <buffer>]

1111

Comparison with SQL

• 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)

1212

TinySQL Example

SELECTSELECT nodeid, nestNo, lightnodeid, nestNo, lightFROMFROM sensorssensorsWHEREWHERE light > 400light > 400SAMPLE PRIODSAMPLE PRIOD 1s 1s FORFOR 2s 2s

2

1

2

1

nodeinodeidd

405251

422171

389250

455170

lightlightnestNnestNoo

EpocEpochh

Sensors

“Find the sensors in bright nests.”

1313

Event-based Queries

• ON event SELECT …

• Run query only when interesting events happen

• Event examples- Button pushedButton pushed

- Message arrivalMessage arrival

- Bird enters nestBird enters nest

• Analogous to triggers but events are user-defined

1414

Query over Stored Data

• Named buffers in Flash memory• Store query results in buffers• Query over named buffers• Analogous to materialized views• Example:

- CREATE BUFFERCREATE BUFFER name SIZE x (field1 type1, field2 type2, …)name SIZE x (field1 type1, field2 type2, …)

- SELECTSELECT a1, a2 FROM sensorsa1, a2 FROM sensors SAMPLESAMPLE PERIODPERIOD dd INTOINTO namename

- SELECTSELECT field1, field2, …field1, field2, … FROMFROM namename SAMPLE PERIODSAMPLE PERIOD dd


ArchitectureII



Query ProcessingV

ConclusionsVI

1616

Tree-based Routing

• Tree-based routing- Used in:Used in:

– Query delivery – Data collection– In-network aggregation

A

B C

D

FE

Q:SELECT …

Q Q

Q

QQ

Q

Q

QQ

Q QQ

R:{…}

R:{…}

R:{…}

R:{…}

R:{…}

http://webs.cs.berkeley.edu/tinyos

1717

Basic Aggregation

• In each epoch:- Each node samples local sensors onceEach node samples local sensors once- Generates Generates partial state record (PSR)partial state record (PSR)

– local readings – readings from children

- Outputs PSR during assigned Outputs PSR during assigned comm. intervalcomm. interval

• At end of epoch, PSR for whole network output at root

• New result on each successive epoch

1

2 3

4

5

1818

Illustration: Aggregation

1

2

3

14

4

54321

1

2 3

4

51

Sensor #

Inte

rval

#

Interval 4SELECT COUNT(*) FROM sensors

Epoch

1919


1

2

23

14

4

54321

1

2 3

4

5

2

Sensor #Interval 3

SELECT COUNT(*) FROM sensors

Inte

rval

#

2020


1

312

23

14

4

54321

1

2 3

4

5

31

Sensor #Interval 2


Inte

rval

#

2121


51

312

23

14

4

54321

1

2 3

4

5

5

Sensor #Interval 1

Inte

rval

#


2222


51

312

23

14

14

54321

1

2 3

4

51

Sensor #

SELECT COUNT(*) FROM sensors Interval 4

Inte

rval

#


ArchitectureII



Query ProcessingV

ConclusionsVI

2424

Inside TinyDB

TinyOSSchema

Query Processor

Multihop Network

Filterlight >

400get (‘temp’)

Aggavg(tem

p)

QueriesSELECT AVG(temp) WHERE light > 400

ResultsT:1, AVG: 225T:2, AVG: 250

Tables

Samples

got(‘temp’)Name: tempTime to sample: 50 uSCost to sample: 90 uJCalibration Table: 3Units: Deg. FError: ± 5 Deg FGet f : getTempFunc()…

getTempFunc(…)getTempFunc(…)

TinyDBTinyDB

2525

Acquisitional Query Processing (ACQP)

• TinyDB acquires AND processes data- Could generate an infinite number of samplesCould generate an infinite number of samples

• An acqusitional query processor controls - when, when,

- where, where,

- and with what frequency data is collected!and with what frequency data is collected!

• Versus traditional systems where data is provided a priori

2626

ACQP: What’s Different?

• How should the query be processed?- Sampling as a first class operationSampling as a first class operation

• How does the user control acquisition?- Rates or lifetimesRates or lifetimes- Event-based triggersEvent-based triggers

• Which nodes have relevant data?- Index-like data structuresIndex-like data structures

• Which samples should be transmitted?- Prioritization, summary, and rate controlPrioritization, summary, and rate control

2727

• E(sampling mag) >> E(sampling light)

1500 uJ vs. 90 uJ

Operator Ordering: Interleave Sampling + Selection

SELECT light, magFROM sensorsWHERE pred1(mag)AND pred2(light)SAMPLE PERIOD 1s

(pred1)

(pred2)

mag light

Traditional DBMS

At 1 sample / sec, total power savings could be as much as 3.5mW Comparable to processor!

(pred1)

(pred2)

mag

light

(pred1)

(pred2)

mag

lightACQP

Correct orderingCorrect ordering(unless pred1 is (unless pred1 is very very selective selective

and pred2 is not):and pred2 is not):

Cheap

Costly

2828

Conclusions

• Architecture

• Data Model and Query Language

• Query Dissemination and Result Collection

• Query Processing

Questions ?

Documents

W. Hong & S. Madden – Implementation and Research Issues in Query Processing for Wireless Sensor Networks, ICDE 2004