Querying Sensor Networks

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Querying Sensor Networks

Sam MaddenUC Berkeley

November 18th, 2002 @ Madison

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Introduction• What are sensor networks?• Programming Sensor Networks

Is Hard– Especially if you want to build a

“real” application » Example: Vehicle tracking application

took 2 grad students 2 weeks to build and hundreds of lines of code.

• Declarative Queries Are Easy– And, can be faster and more

robust than most applications! » Vehicle tracking query: took 2

minutes to write, worked just as well!

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

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Overview• Sensor Networks• Why Queries in Sensor Nets• TinyDB

– Features– Demo

• Focus: Tiny Aggregation• The Next Step

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Device Capabilities• “Mica Motes”

– 8bit, 4Mhz processor» Roughly a PC AT

– 40kbit radio» Time to send 1 bit = 800 instrs» Reducing communication is good

– 4KB RAM, 128K flash, 512K EEPROM– Sensor board expansion slot

» Standard board has light & temperature sensors, accelerometer, magnetometer, microphone, & buzzer

• Other more powerful platforms exist– E.g. Sensoria WINS nodes

• Trend towards smaller devices– “Smart Dust” – Kris Pister, et al.

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Sensor Net Sample Apps

Habitat Monitoring. Storm petrels on great duck island, microclimates on James Reserve.

Traditional monitoring apparatus.

Earthquake monitoring in shake-test sites.

Vehicle detection: sensors dropped from UAV along a road, collect data about passing vehicles, relay data back to UAV.

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Key Constraint: Power• Lifetime from One

pair of AA batteries – 2-3 days at full

power– 6 months at 2%

duty cycle

• Communication dominates cost– Because it takes so

long (~30ms) to send / receive a message

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TinyOS• Operating system from David Culler’s

group at Berkeley• C-like programming environment

• Provides messaging layer, abstractions for major hardware components– Split phase highly asynchronous, interrupt-

driven programming model

Hill, Szewczyk, Woo, Culler, & Pister. “Systems Architecture Directions for Networked Sensors.” ASPLOS 2000. See http://webs.cs.berkeley.edu/tos

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Communication In Sensor Nets

• Radio communication has high link-level losses– typically about 20% @ 5m

• Newest versions of TinyOS provide link-level acknowledgments

• No end-to-end acknowledgements

• Ad-hoc neighbor discovery

• Two major routing techniques: tree-based hierarchy and geographic

A

B C

DFE

00 10 21

10 11 12

20 21 22

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Declarative Queries for Sensor Networks

• Examples:SELECT nodeid, lightFROM sensorsWHERE light > 400SAMPLE PERIOD 1s

1

2SELECT roomNo, AVG(volume)FROM sensorsGROUP BY roomNoHAVING AVG(volume) > 200

Rooms w/ volume > 200

“epoch”

453 245 512 …Light Temp Accel ….

442 278 513 …406 335 511 …

T-2T-1

T

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Declarative Benefits In Sensor Networks

• Vastly simplifies execution for large networks– Since locations are described by predicates– Operations are over groups

• Enables tolerance to faults– Since system is free to choose where and

when operations happen• Data independence

– System is free to choose where data lives, how it is represented

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Computing In Sensor Nets Is Hard

• Why?– Limited power (must optimize for it!)– Lossy communication– Zero administration – Limited processing capabilities, storage, bandwidth

• In power-based optimization, we choose:» Where data is processed.» How data is routed

• Exploit operator semantics!• Avoid dead nodes

» How to order operators, sampling, etc.» What kinds of indices to apply, which data to prioritize …

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TinyDB• A distributed query processor for

networks of Mica motes– Available today!

• Goal: Eliminate the need to write C code for most TinyOS users

• Features– Declarative queries– Temporal + spatial operations– Multihop routing– In-network storage

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A

B C

DFE

TinyDB @ 10000 FtQuery

{D,E,F}

{B,D,E,F}

{A,B,C,D,E,F}

Written in SQL-Like Language With Extensions For :•Sample rate•Offline delivery•Temporal Aggregation

(Almost) All Queries are Continuous and Periodic

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TinyDB Demo

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Applications + Early Adopters

• Some demo apps:– Network monitoring– Vehicle tracking

• “Real” future deployments:– Environmental monitoring @ GDI (and James

Reserve?)– Generic Sensor Kit– Building Monitoring

Demo!

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TinyDB Architecture (Per node)

Radio Stack

Schema

TinyAllloc

TupleRouter

AggOperatorSelOperator

Network

TupleRouter:•Fetches readings (for ready queries)•Builds tuples•Applies operators•Deliver results (up tree)

AggOperator:•Combines local & neighbor readings

SelOperator:•Filters readings

Schema:•“Catalog” of commands & attributes (more later)

TinyAlloc:•Reusable memory allocator!

~10,000 Lines C Code~5,000 Lines Java~3200 Bytes RAM (w/ 768 byte heap)~58 kB compiled code(3x larger than 2nd largest TinyOS Program)

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TAG• In-network processing of aggregates

– Aggregates are common operation– Reduces costs depending on type of

aggregates– Focus on “spatial aggregation” (Versus

“temporal aggregation”)

• Exploitation of operator, functional semantics

Tiny AGgregation (TAG), Madden, Franklin, Hellerstein, Hong. OSDI 2002 (to appear).

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Aggregation Framework• As in extensible databases, we support any

aggregation function conforming to:

Aggn={fmerge, finit, fevaluate}Fmerge{<a1>,<a2>} <a12>finit{a0} <a0>Fevaluate{<a1>} aggregate value(Merge associative, commutative!)

Example: AverageAVGmerge {<S1, C1>, <S2, C2>} < S1 + S2 , C1 + C2>AVGinit{v} <v,1>AVGevaluate{<S1, C1>} S1/C1

Partial State Record (PSR)

Just like parallel database systems – e.g. Bubba!

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Query Propagation Review

A

B C

D

FE

SELECT AVG(light)…

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Pipelined Aggregates• After query propagates, during each epoch:

– Each sensor samples local sensors once– Combines them with PSRs from children– Outputs PSR representing aggregate state

in the previous epoch.• After (d-1) epochs, PSR for the whole tree

output at root– d = Depth of the routing tree– If desired, partial state from top k levels

could be output in kth epoch• To avoid combining PSRs from different

epochs, sensors must cache values from children

1

2 3

4

5Value from 5 produced at

time t arrives at 1 at time

(t+3)

Value from 2 produced at


(t+1)

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Illustration: Pipelined Aggregation

1

2 3

4

5

SELECT COUNT(*) FROM sensors

Depth = d

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1 2 3 4 5

1 1 1 1 1 1

1

2 3

4

51

1

11

1Sensor #

Epoc

h #

Epoch 1SELECT COUNT(*) FROM sensors

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1 2 3 4 5

1 1 1 1 1 12 3 1 2 2 1

1

2 3

4

51

2

21

3Sensor #

Epoc

h #


28


1 2 3 4 5

1 1 1 1 1 12 3 1 2 2 13 4 1 3 2 1

1

2 3

4

51

2

31

4Sensor #

Epoc

h #


29


1 2 3 4 5

1 1 1 1 1 12 3 1 2 2 13 4 1 3 2 14 5 1 3 2 1

1

2 3

4

51

2

31

5Sensor #

Epoc

h #


30


1 2 3 4 5

1 1 1 1 1 12 3 1 2 2 13 4 1 3 2 14 5 1 3 2 15 5 1 3 2 1

1

2 3

4

51

2

31

5Sensor #

Epoc

h #


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Grouping• If query is grouped, sensors apply

predicate on each epoch• PSRs tagged with group• When a PSR (with group) is received:

– If it belongs to a stored group, merge with existing PSR

– If not, just store it• At the end of each epoch, transmit one

PSR per group

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Group Eviction• Problem: Number of groups in any one iteration

may exceed available storage on sensor• Solution: Evict! (Partial Preaggregation*)

– Choose one or more groups to forward up tree– Rely on nodes further up tree, or root, to recombine

groups properly– What policy to choose?

» Intuitively: least popular group, since don’t want to evict a group that will receive more values this epoch.

» Experiments suggest:• Policy matters very little• Evicting as many groups as will fit into a single message is good

* Per-Åke Larson. Data Reduction by Partial Preaggregation. ICDE 2002.

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TAG Advantages• In network processing reduces

communication– Important for power and contention

• Continuous stream of results– In the absence of faults, will converge to

right answer• Lots of optimizations

– Based on shared radio channel– Semantics of operators

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Simulation Environment• Chose to simulate to allow 1000’s of nodes and

control of topology, connectivity, loss• Java-based simulation & visualization for

validating algorithms, collecting data.• Coarse grained event based simulation

– Sensors arranged on a grid, radio connectivity by Euclidian distance

– Communication model» Lossless: All neighbors hear all messages» Lossy: Messages lost with probability that increases with

distance» Symmetric links» No collisions, hidden terminals, etc.

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Simulation ResultTotal Bytes Xmitted vs. Aggregation Func tion

0100002000030000400005000060000700008000090000

100000

EXTERNAL MAX AVERAGE COUNT MEDIANAggregation Function

Tota

l Byt

es X

mitt

ed

Simulation Results2500 Nodes50x50 GridDepth = ~10Neighbors = ~20

Some aggregates require dramatically more state!

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Taxonomy of Aggregates• TAG insight: classify aggregates according to

various functional properties– Yields a general set of optimizations that can

automatically be appliedProperty Examples Affects

Partial State MEDIAN : unbounded, MAX : 1 record

Effectiveness of TAG

Duplicate Sensitivity

MIN : dup. insensitive,AVG : dup. sensitive

Routing Redundancy

Exemplary vs. Summary

MAX : exemplaryCOUNT: summary

Applicability of Sampling, Effect of Loss

Monotonic COUNT : monotonicAVG : non-monotonic

Hypothesis Testing, Snooping

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Optimization: Channel Sharing (“Snooping”)

• Insight: Shared channel enables optimizations• Suppress messages that won’t affect aggregate

– E.g., in a MAX query, sensor with value v hears a neighbor with value ≥ v, so it doesn’t report

– Applies to all exemplary, monotonic aggregates» Can be applied to summary aggregates also if imprecision is

allowed

• Learn about query advertisements it missed– If a sensor shows up in a new environment, it can

learn about queries by looking at neighbors messages.» Root doesn’t have to explicitly rebroadcast query!

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Optimization: Hypothesis Testing

• Insight: Root can provide information that will suppress readings that cannot affect the final aggregate value.– E.g. Tell all the nodes that the MIN is definitely <

50; nodes with value ≥ 50 need not participate.– Depends on monotonicity

• How is hypothesis computed?– Blind guess– Statistically informed guess– Observation over first few levels of tree / rounds of aggregate

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Experiment: Hypothesis Testing

Uniform Value Distribution, Dense Packing, Ideal Communication

Messages/ Epoch vs. Network Diameter(SELECT MAX(attr), R(attr) = [0,100])

0

500

1000

1500

2000

2500

3000

10 20 30 40 50Network Diameter

Mes

sage

s /

Epoc

h

No GuessGuess = 50Guess = 90Snooping

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Optimization: Use Multiple Parents

• For duplicate insensitive aggregates• Or aggregates that can be expressed as a linear

combination of parts– Send (part of) aggregate to all parents– Decreases variance

» Dramatically, when there are lots of parents

A

B C

A

B C

A

B C

1A

B C

A

B C

1/2 1/2

No splitting:

E(count) = c * p

Var(count) = c2 * p * (1-p)

With Splitting:

E(count) = 2 * c/2 * p

Var(count) = 2 * (c/2)2 * p * (1-p)

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Multiple Parents Results• Interestingly, this

technique is much better than previous analysis predicted!

• Losses aren’t independent!

• Instead of focusing data on a few critical links, spreads data over many links

Benefit of Result Splitting (COUNT query)

0

200

400

600

800

1000

1200

1400

(2500 nodes, lossy radio model, 6 parents per node)

Avg.

CO

UNT Splitting

No Splitting

Critical Link!

No Splitting With Splitting

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Fun Stuff• Sophisticated, sensor network

specific aggregates

• Temporal aggregates

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Temporal Aggregates• TAG was about “spatial” aggregates

– Inter-node, at the same time• Want to be able to aggregate across time as

well• Two types:

– Windowed: AGG(size,slide,attr)

– Decaying: AGG(comb_func, attr)– Demo!

… R1 R2 R3 R4 R5 R6 …

slide =2 size =4

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Isobar Finding

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TAG Summary• In-network query processing a big win for

many aggregate functions• By exploiting general functional properties

of operators, optimizations are possible– Requires new aggregates to be tagged with

their properties

• Up next: non-aggregate query processing optimizations – a flavor of things to come!

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Acquisitional Query Processing

• Cynical question: what’s really different about sensor networks?

–Low Power?–Lots of Nodes?–Limited Processing Capabilities?

Laptops!Distributed DBs!

Moore’s Law!

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Answer• Long running queries on physically

embedded devices that control when and and with what frequency data is collected!

• Versus traditional systems where data is provided a priori– Next: an acquisitional teaser…

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ACQP: What’s Different?• How does the user control acquisition?

– Specify rates or lifetimes– Trigger queries in response to events

• Which nodes have relevant data?– Need a node index– Construct topology such that nodes that are queried together

route together• What sensors should be sampled?

– Treat sampling at an operator– Sample cheapest sensors first

• Which samples should be transmitted?– Not all of them, if bandwidth or power is limited– Those that are most “valuable”?

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Operator Ordering: Interleave Sampling + Selection

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

• Energy cost of sampling mag >> cost of sampling light•1500 uJ vs. 90 uJ

• Correct ordering (unless pred1 is very selective):

At 1 sample / sec, total power savings could be as much as 4mW, same as the processor!

2. Sample light Apply pred2Sample magApply pred1

1. Sample light Sample magApply pred1Apply pred2

3. Sample mag Apply pred1Sample lightApply pred2

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Optimizing in ACQP• Model sampling as an “expensive predicate” • Some subtleties:

– Attributes referenced in multiple predicates; which to “charge”?

– Attributes must be fetched before operators that use them can be applied

• Solution: – Treat sampling as a separate task– Build a partial order on sampling and predicates – Solve for cheapest schedule using series-parallel scheduling

algorithm (Monma & Sidney, 1979.), as in other optimization work (e.g. Ibaraki & Kameda, TODS, 1984, or Hellerstein, TODS, 1998.)

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Exemplary Aggregate Pushdown

SELECT WINMAX(light,8s,8s)FROM sensorsWHERE mag > xSAMPLE INTERVAL 1s

Unless > x is very selective, correct ordering is:Sample lightCheck if it’s the maximumIf it is:

Sample magCheck predicateIf satisfied, update maximum

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Summary• Declarative queries are the right interface for

data collection in sensor nets!• Aggregation is a fundamental operation for which

there are many possible optimizations– Network Aware Techniques

• Current Research: Acquisitional Query Processing – Framework for addresses lots of the new issues that

arise in sensor networks, e.g.» Order of sampling and selection» Languages, indices, approximations that give user control

over which data enters the system

TinyDB Release Available - http://telegraph.cs.berkeley.edu/tinydb

http://telegraph.cs.berkeley.edu/tinydb








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Questions?

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Simulation Screenshot

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TinyAlloc• Handle Based Compacting Memory Allocator• For Catalog, Queries

Free Bitmap

HeapMaster Pointer

Table

Handle h;

call MemAlloc.alloc(&h,10);

…

(*h)[0] = “Sam”;

call MemAlloc.lock(h);

tweakString(*h);

call MemAlloc.unlock(h);

call MemAlloc.free(h);

User Program

Free Bitmap

HeapMaster Pointer

Table

Free Bitmap

HeapMaster Pointer

Table

Free Bitmap

HeapMaster Pointer

Table

Compaction

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Schema• Attribute & Command IF

– At INIT(), components register attributes and commands they support

» Commands implemented via wiring» Attributes fetched via accessor command

– Catalog API allows local and remote queries over known attributes / commands.

• Demo of adding an attribute, executing a command.

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Q1: Expressiveness• Simple data collection satisfies most

users• How much of what people want to do is

just simple aggregates?– Anecdotally, most of it– EE people want filters + simple statistics

(unless they can have signal processing)• However, we’d like to satisfy everyone!

http://images.google.com/imgres?imgurl=www.nrc.nl/W2/Lab/Housekunst/happiness.jpg&imgrefurl=http://www.nrc.nl/W2/Lab/Housekunst/&h=223&w=250&prev=/images%3Fq%3Dhappiness%26start%3D80%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26sa%3DN

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Query Language• New Features:

– Joins– Event-based triggers

»Via extensible catalog– In network & nested queries– Split-phase (offline) delivery

»Via buffers

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Sample Query 1Bird counter:CREATE BUFFER birds(uint16 cnt)

SIZE 1

ON EVENT bird-enter(…)SELECT b.cnt+1FROM birds AS bOUTPUT INTO bONCE

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Sample Query 2Birds that entered and left within time t of each other:

ON EVENT bird-leave AND bird-enter WITHIN tSELECT bird-leave.time, bird-leave.nestWHERE bird-leave.nest = bird-enter.nestONCE

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Sample Query 3Delta compression:

SELECT light FROM buf, sensorsWHERE |s.light – buf.light| > tOUTPUT INTO bufSAMPLE PERIOD 1s

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Sample Query 4Offline Delivery + Event ChainingCREATE BUFFER equake_data( uint16 loc, uint16 xAccel, uint16 yAccel)

SIZE 1000PARTITION BY NODE

SELECT xAccel, yAccelFROM SENSORSWHERE xAccel > t OR yAccel > tSIGNAL shake_start(…)SAMPLE PERIOD 1sON EVENT shake_start(…)

SELECT loc, xAccel, yAccelFROM sensorsOUTPUT INTO BUFFER equake_data(loc, xAccel, yAccel)SAMPLE PERIOD 10ms

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Event Based Processing• Enables internal and chained actions• Language Semantics

– Events are inter-node– Buffers can be global

• Implementation plan– Events and buffers must be local– Since n-to-n communication not (well)

supported• Next: operator expressiveness

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Attribute Driven Topology Selection

• Observation: internal queries often over local area*– Or some other subset of the network

»E.g. regions with light value in [10,20]• Idea: build topology for those queries

based on values of range-selected attributes– Requires range attributes, connectivity to be

relatively static* Heideman et. Al, Building Efficient Wireless Sensor Networks With Low Level Naming. SOSP, 2001.

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Attribute Driven Query Propagation

1 2 3

4

[1,10][7,15]

[20,40]

SELECT …

WHERE a > 5 AND a < 12

Precomputed intervals == “Query Dissemination Index”

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Attribute Driven Parent Selection

1 2 3

4

[1,10] [7,15] [20,40]

[3,6]

[3,6] [1,10] = [3,6]

[3,7] [7,15] = ø

[3,7] [20,40] = ø

Even without intervals, expect that sending to parent with closest value will help

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Hot off the press…Nodes Vi s i t ed vs. Range Quer y Si ze f or

Di ff er ent I ndex Pol i ci es

050

100150200250300350400450

0.001 0.05 0.1 0.2 0.5 1Quer y Si ze as % of Val ue Range

( Random val ue di st r i but i on, 20x20 gr i d, i deal connect i vi t y t o ( 8) nei ghbor s)

Numb

er o

f No

des

Visi

ted

(400

= M

ax)

B est Case (Expec ted)C loses t ParentNeares t V alueSnooping

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Grouping• GROUP BY expr

– expr is an expression over one or more attributes» Evaluation of expr yields a group number» Each reading is a member of exactly one group

Example: SELECT max(light) FROM sensorsGROUP BY TRUNC(temp/10)

Sensor ID Light Temp Group1 45 25 22 27 28 23 66 34 34 68 37 3

Group max(light)2 453 68

Result:

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Having• HAVING preds

– preds filters out groups that do not satisfy predicate

– versus WHERE, which filters out tuples that do not satisfy predicate

– Example: SELECT max(temp) FROM sensors GROUP BY light HAVING max(temp) < 100

Yields all groups with temperature under 100

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Group Eviction• Problem: Number of groups in any one iteration may

exceed available storage on sensor• Solution: Evict!

– Choose one or more groups to forward up tree– Rely on nodes further up tree, or root, to recombine groups

properly– What policy to choose?

» Intuitively: least popular group, since don’t want to evict a group that will receive more values this epoch.

» Experiments suggest:• Policy matters very little• Evicting as many groups as will fit into a single message is good

72

Experiment: Basic TAG

Dense Packing, Ideal Communication

Bytes / Epoch vs. Network Diameter

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000


Avg.

Byt

es /

Epo

ch

COUNTMAXAVERAGEMEDIANEXTERNALDISTINCT

73

Experiment: Hypothesis Testing

Uniform Value Distribution, Dense Packing, Ideal Communication

Messages/ Epoch vs. Network Diameter

0

500

1000

1500

2000

2500

3000


Mes

sage

s /

Epoc

h

No GuessGuess = 50Guess = 90Snooping

74

Experiment: Effects of Loss

Percent Error From Single Loss vs. Network Diameter

0

0.5

1

1.5

2

2.5

3

3.5


Perc

ent E

rror

Fro

m S

ingl

e Lo

ss

AVERAGECOUNTMAXMEDIAN

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Experiment: Benefit of Cache

Percentage of Network I nvolved vs. Network Diameter

0

0.2

0.4

0.6

0.8

1

1.2


% N

etw

ork

No Cache5 Rounds Cache9 Rounds Cache15 Rounds Cache

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Pipelined Aggregates• After query propagates, during each epoch:

– Each sensor samples local sensors once– Combines them with PSRs from children– Outputs PSR representing aggregate state in

the previous epoch.• After (d-1) epochs, PSR for the whole tree

output at root– d = Depth of the routing tree– If desired, partial state from top k levels

could be output in kth epoch• To avoid combining PSRs from different epochs,

sensors must cache values from children

1

2 3

4

5Value from 5 produced at


(t+3)

Value from 2 produced at


(t+1)

77

Pipelining Example

1

2

43

5

SID Epoch Agg.

SID Epoch Agg.

SID Epoch Agg.

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Pipelining Example

1

2

43

5

SID Epoch Agg.2 0 14 0 1

SID Epoch Agg.1 0 1

SID Epoch Agg.3 0 15 0 1

Epoch 0

<5,0,1>

<4,0,1>

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Pipelining Example

1

2

43

5

SID Epoch Agg.2 0 14 0 12 1 14 1 13 0 2

SID Epoch Agg.1 0 11 1 12 0 2

SID Epoch Agg.3 0 15 0 13 1 15 1 1

Epoch 1

<5,1,1>

<4,1,1><3,0,2>

<2,0,2>

80

Pipelining Example

1

2

43

5

SID Epoch Agg.2 0 14 0 12 1 14 1 13 0 22 2 14 2 13 1 2

SID Epoch Agg.1 0 11 1 12 0 21 2 12 0 4

SID Epoch Agg.3 0 15 0 13 1 15 1 13 2 15 2 1

Epoch 2

<5,2,1>

<4,2,1><3,1,2>

<2,0,4>

<1,0,3>

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Pipelining Example

1

2

43

5

SID Epoch

Agg.

2 0 14 0 12 1 14 1 13 0 22 2 14 2 13 1 2

SID Epoch Agg.1 0 11 1 12 0 21 2 12 0 4

SID Epoch Agg.3 0 15 0 13 1 15 1 13 2 15 2 1

Epoch 3

<5,3,1>

<4,3,1><3,2,2>

<2,1,4>

<1,0,5>

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Pipelining Example

1

2

43

5

Epoch 4

<5,4,1>

<4,4,1><3,3,2>

<2,2,4>

<1,1,5>

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Our Stream Semantics• One stream, ‘sensors’• We control data rates• Joins between that stream and buffers are

allowed• Joins are always landmark, forward in time, one

tuple at a time– Result of queries over ‘sensors’ either a single tuple

(at time of query) or a stream• Easy to interface to more sophisticated systems• Temporal aggregates enable fancy window

operations

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Formal Spec.ON EVENT <event> [<boolop> <event>... WITHIN <window>]

[SELECT {<expr>|agg(<expr>)|temporalagg(<expr>)} FROM [sensors | <buffer> | events]] [WHERE {<pred>}] [GROUP BY {<expr>}] [HAVING {<pred>}] [ACTION [<command> [WHERE <pred>] |

BUFFER <bufname> SIGNAL <event>({<params>}) | (SELECT ... ) [INTO BUFFER <bufname>]]]

[SAMPLE PERIOD <seconds> [FOR <nrounds>] [INTERPOLATE <expr>] [COMBINE {temporal_agg(<expr>)}] |

ONCE]

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Buffer Commands

[AT <pred>:]CREATE [<type>] BUFFER <name> ({<type>})PARTITION BY [<expr>]SIZE [<ntuples>,<nseconds>][AS SELECT ...

[SAMPLE PERIOD <seconds>]]

DROP BUFFER <name>

Querying Sensor Networks

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