Implementation and Research Issues in Query Processing for Wireless Sensor Networks

1

Implementation and Research Issues in Query Processing for Wireless

Sensor Networks

Wei Hong Intel Research, Berkeley

[email protected]

Sam MaddenMIT

[email protected]

Adapted by L.B.

2

Declarative Queries

• Programming Apps is Hard– Limited power budget– Lossy, low bandwidth communication– Require long-lived, zero admin deployments– Distributed Algorithms– Limited tools, debugging interfaces

• Queries abstract away much of the complexity– Burden on the database developers– Users get:

• Safe, optimizable programs• Freedom to think about apps instead of details

3

TinyDB: Prototype declarativequery processor

• Platform: Berkeley Motes + TinyOS• Continuous variant of SQL : TinySQL

• Power and data-acquisition based in-network optimization framework

• Extensible interface for aggregates, new types of sensors

4

TinyDB RevisitedSELECT 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

TinyDB

Query, Trigger

Data

5

Feature Overview

• Declarative SQL-like query interface• Metadata catalog management• Multiple concurrent queries• Network monitoring (via queries)• In-network, distributed query processing• Extensible framework for attributes,

commands and aggregates• In-network, persistent storage

6

TinyDB GUI

TinyDB Client APIDBMS

Sensor network

Architecture

TinyDB query processor

0

4

0

1

5

2

6

3

7

JDBC

Mote side

PC side

8

7

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, timestamp, queue

length, etc.• Nodes return NULL for unknown attributes• On server, all attributes are defined in catalog.xml• Discussion: other alternative data models?

8

Query Language (TinySQL)

SELECT <aggregates>, <attributes>

[FROM {sensors | <buffer>}][WHERE <predicates>][GROUP BY <exprs>][SAMPLE PERIOD <const> |

ONCE][INTO <buffer>][TRIGGER ACTION <command>]

9

Comparison with SQL

• Single table in FROM clause• Only conjunctive comparison predicates

in WHERE and HAVING• No subqueries• No column alias in SELECT clause• Arithmetic expressions limited to

column op constant• Only fundamental difference: SAMPLE

PERIOD clause

10

TinySQL Examples

SELECT nodeid, nestNo, lightFROM sensorsWHERE light > 400EPOCH DURATION 1s

1EpocEpoc

hhNodeiNodei

ddnestNnestN

ooLightLight

0 1 17 455

0 2 25 389

1 1 17 422

1 2 25 405

Sensors

“Find the sensors in bright nests.”

11

TinySQL Examples (cont.)

Epoch region CNT(…) AVG(…)

0 North 3 360

0 South 3 520

1 North 3 370

1 South 3 520

“Count the number occupied nests in each loud region of the island.”

SELECT region, CNT(occupied) AVG(sound)

FROM sensors

GROUP BY region

HAVING AVG(sound) > 200

EPOCH DURATION 10s

3

Regions w/ AVG(sound) > 200

SELECT AVG(sound)

FROM sensors

EPOCH DURATION 10s

2

12

Event-based Queries

• ON event SELECT …• Run query only when interesting events

happens• Event examples

– Button pushed– Message arrival– Bird enters nest

• Analogous to triggers but events are user-defined

13

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

14

Inside TinyDB

TinyOS

Schema

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

~10,000 Lines Embedded C Code

~5,000 Lines (PC-Side) Java

~3200 Bytes RAM (w/ 768 byte heap)

~58 kB compiled code

(3x larger than 2nd largest TinyOS Program)

15

Tree-based Routing

• Tree-based routing– Used in:

• Query delivery • Data collection• In-network aggregation

– Relationship to indexing?

A

B C

D

FE

Q:SELECT …

Q Q

Q

QQ

Q

Q

Q

Q

Q QQ

R:{…}

R:{…}

R:{…}

R:{…} R:{…}

16

Sensor Network Research

• Very active research area– Can’t summarize it all

• Focus: database-relevant research topics– Some outside of Berkeley– Other topics that are itching to be scratched– But, some bias towards work that we find

compelling

17

Topics

• In-network aggregation• Acquisitional Query Processing• Heterogeneity• Intermittent Connectivity• In-network Storage• Statistics-based summarization and

sampling• In-network Joins• Adaptivity and Sensor Networks• Multiple Queries

18

Topics



19

Tiny Aggregation (TAG)

• In-network processing of aggregates– Common data analysis operation

• Aka gather operation or reduction in || programming

– Communication reducing• Operator dependent benefit

– Across nodes during same epoch

• Exploit query semantics to improve efficiency!

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

20

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

• Extras:– Predicate-based partitioning via GROUP BY

1

2 3

4

5

21

Illustration: Aggregation

1 2 3 4 5

4 1

3

2

1

4

1

2 3

4

5

1

Sensor #

Inte

rval #

Interval 4SELECT COUNT(*) FROM sensors

Epoch

22


1 2 3 4 5

4 1

3 2

2

1

4

1

2 3

4

5

2

Sensor #


Inte

rval #

23


1 2 3 4 5

4 1

3 2

2 1 3

1

4

1

2 3

4

5

31

Sensor #


Inte

rval #

24


1 2 3 4 5

4 1

3 2

2 1 3

1 5

4

1

2 3

4

5

5

Sensor #

SELECT COUNT(*) FROM sensors Interval 1

Inte

rval #

25


1 2 3 4 5

4 1

3 2

2 1 3

1 5

4 1

1

2 3

4

5

1

Sensor #

SELECT COUNT(*) FROM sensors Interval 4

Inte

rval #

26

Aggregation Framework

• As in extensible databases, TinyDB supports any aggregation function conforming to:

Aggn={finit, fmerge, fevaluate}

Finit {a0} <a0>

Fmerge {<a1>,<a2>} <a12>

Fevaluate {<a1>} aggregate value

Example: AverageAVGinit {v} <v,1>

AVGmerge {<S1, C1>, <S2, C2>} < S1 + S2 , C1 + C2>

AVGevaluate{<S, C>} S/C

Partial State Record (PSR)

Restriction: Merge associative, commutative

27

Property Examples Affects

Partial State MEDIAN : unbounded, MAX : 1 record

Effectiveness of TAG

Monotonicity COUNT : monotonicAVG : non-monotonic

Hypothesis Testing, Snooping

Exemplary vs. Summary

MAX : exemplaryCOUNT: summary

Applicability of Sampling, Effect of Loss

Duplicate Sensitivity

MIN : dup. insensitive,AVG : dup. sensitive

Routing Redundancy

Taxonomy of Aggregates

• TAG insight: classify aggregates according to various functional properties– Yields a general set of optimizations that can automatically be

applied

Drives an API!

28

Use Multiple Parents

• Use graph structure – Increase delivery probability with no communication

overhead

• For duplicate insensitive aggregates, or• Aggs expressible as sum of parts

– Send (part of) aggregate to all parents• In just one message, via multicast

– Assuming independence, decreases variance

SELECT COUNT(*)

A

B C

R

A

B C

c

R

P(link xmit successful) = p

P(success from A->R) = p2

E(cnt) = c * p2

Var(cnt) = c2 * p2 * (1 – p2) V

# of parents = n

E(cnt) = n * (c/n * p2)

Var(cnt) = n * (c/n)2 * p2 * (1 – p2) = V/n

A

B C

c/n c/n

R

n = 2

29

Multiple Parents Results

• Better than previous analysis expected!

• Losses aren’t independent!

• Insight: 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

. C

OU

NT Splitting

No Splitting

Critical Link!

No Splitting With Splitting

30

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

Madden, Franklin, Hellerstein, and Hong. The Design of An Acqusitional Query Processor. SIGMOD, 2003.

31

ACQP: What’s 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

32

• 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)EPOCH DURATION 1s

(pred1)

(pred2)

mag

light

(pred1)

(pred2)

mag

light

(pred1)

(pred2)

mag light

Traditional DBMS

ACQP

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

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

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

Cheap

Costly

33

Exemplary Aggregate Pushdown

SELECT WINMAX(light,8s,8s)FROM sensorsWHERE mag > xEPOCH DURATION 1s

• Novel, general pushdown technique

• Mag sampling is the most expensive operation!

WINMAX

(mag>x)

mag light

Traditional DBMS

light

mag

(mag>x)

WINMAX

(light > MAX)

ACQP

34

Topics

• In-network aggregation• Acquisitional Query Processing• Heterogeneity• Intermittent Connectivity• In-network Storage• Statistics-based summarization and sampling• In-network Joins• Adaptivity and Sensor Networks• Multiple Queries

35

Heterogeneous Sensor Networks

• Leverage small numbers of high-end nodes to benefit large numbers of inexpensive nodes

• Still must be transparent and ad-hoc• Key to scalability of sensor networks• Interesting heterogeneities

– Energy: battery vs. outlet power– Link bandwidth: Chipcon vs. 802.11x– Computing and storage: ATMega128 vs.

Xscale– Pre-computed results– Sensing nodes vs. QP nodes

36

Computing Heterogeneity with TinyDB

• Separate query processing from sensing– Provide query processing on a small number of nodes– Attract packets to query processors based on “service

value”• Compare the total energy consumption of the

network

• No aggregation• All aggregation• Opportunistic aggregation• HSN proactive

aggregation

Mark Yarvis and York Liu, Intel’s Heterogeneous Sensor

Network Project, ftp://download.intel.com/research/people/HSN_IR_Day_Poster_03.pdf.

37

5x7 TinyDB/HSN Mica2 Testbed

38

Data Packet SavingData Packet Saving

-50.00%

-45.00%

-40.00%

-35.00%

-30.00%

-25.00%

-20.00%

-15.00%

-10.00%

-5.00%

0.00%

1 2 3 4 5 6 All (35)

Number of Aggregator

% C

han

ge

in D

ata

Pac

ket

Co

un

t

Data Packet Saving - Aggregator Placement

-50.00%

-45.00%

-40.00%

-35.00%

-30.00%

-25.00%

-20.00%

-15.00%

-10.00%

-5.00%

0.00%

25 27 29 31 All (35)

Aggregator Location

% C

han

ge

in D

ata

Pac

ket

Co

un

nt

• How many aggregators are desired?

• Does placement matter?

11% aggregators achieve 72% of max

data reduction

Optimal placement 2/3 distance from sink.

39

Occasionally Connected Sensornets

TinyDB QPTinyDB QP

TinyDB Server

GTWY

Mobile GTWYMobile GTWY

TinyDB QP

Mobile GTWY

GTWY

internet

GTWY

40

Occasionally Connected Sensornets Challenges

• Networking support– Tradeoff between reliability, power

consumption and delay– Data custody transfer: duplicates?– Load shedding– Routing of mobile gateways

• Query processing– Operation placement: in-network vs. on mobile

gateways– Proactive pre-computation and data movement

• Tight interaction between networking and QP

Fall, Hong and Madden, Custody Transfer for Reliable Delivery in Delay Tolerant Networks, http://www.intel-research.net/Publications/Berkeley/081220030852_157.pdf.

41

Distributed In-network Storage

• Collectively, sensornets have large amounts of in-network storage

• Good for in-network consumption or caching

• Challenges– Distributed indexing for fast query

dissemination– Resilience to node or link failures– Graceful adaptation to data skews– Minimizing index insertion/maintenance cost

42

Example: DIM• Functionality

– Efficient range query for multidimensional data.

• Approaches– Divide sensor field into

bins.– Locality preserving

mapping from m-d space to geographic locations.

– Use geographic routing such as GPSR.

• Assumptions– Nodes know their

locations and network boundary

– No node mobility

E2= <0.6, 0.7>E1 = <0.7, 0.8>

Q1=<.5-.7, .5-1>

Xin Li, Young Jin Kim, Ramesh Govindan and Wei Hong, Distributed Index for Multi-dimentional Data (DIM) in Sensor Networks, SenSys 2003.

43

Statistical Techniques

• Approximations, summaries, and sampling based on statistics and statistical models

• Applications:– Limited bandwidth and large number of

nodes -> data reduction– Lossiness -> predictive modeling– Uncertainty -> tracking correlations and

changes over time– Physical models -> improved query

answering

44

Correlated Attributes

• Data in sensor networks is correlated; e.g.,– Temperature and voltage– Temperature and light– Temperature and humidity– Temperature and time of day– etc.

45

IDSQ

• Idea: task sensors in order of best improvement to estimate of some value:– Choose leader(s)

• Suppress subordinates• Task subordinates, one at a time

– Until some measure of goodness (error bound) is met

» E.g. “Mahalanobis Distance” -- Accounts for correlations in axes, tends to favor minimizing principal axis

See “Scalable Information-Driven Sensor Querying and Routing for ad hoc Heterogeneous Sensor Networks.” Chu, Haussecker and Zhao. Xerox TR P2001-10113. May, 2001.

46

Model location estimate as a point with 2-dimensional Gaussian uncertainty.

Graphical Representation

Principal Axis

S1

Residual 1

Preferred because it reduces error along principal axis

Residual 2 S2

Area of residuals is equal

47

MQSN: Model-based Probabilistic Querying over

Sensor Networks

Query ProcessorModel

1

23

5

6

4

7

8

9

Joint work with Amol Desphande, Carlos Guestrin,

and Joe Hellerstein

48


Sensor Networks


1

23

5

6

4

7

8

9

Probabilistic Queryselect NodeID, Temp ± 0.1Cwhere NodeID in [1..9] with conf(0.95)

Consult

Model

Observation Plan[Temp, 3], [Temp, 9]

49


Sensor Networks


1

23

5

6

4

7

8

9

Observation Plan[Temp, 3], [Temp, 9]

Probabilistic Queryselect NodeID, Temp ± 0.1Cwhere NodeID in [1..9] with conf(0.95)

Consult

Model

50


Sensor Networks


1

23

5

6

4

7

8

9

Data[Temp, 3] = …, [Temp, 9] = …

Query Results

10

15

20

25

30

1 2 3 4

Node ID

Tem

per

atu

re

Update

Model

51

Challenges

• What kind of models to use ?• Optimization problem:

– Given a model and a query, find the best set of attributes to observe

– Cost not easy to measure• Non-uniform network communication costs• Changing network topologies

– Large plan space• Might be cheaper to observe attributes not in query

– e.g. Voltage instead of Temperature• Conditional Plans:

– Change the observation plan based on observed values

52

MQSN: Current Prototype

• Multi-variate Gaussian Models– Kalman Filters to capture correlations across time

• Handles:– Range predicate queries

• sensor value within [x,y], w/ confidence– Value queries

• sensor value = x, w/in epsilon, w/ confidence– Simple aggregate queries

• AVG(sensor value) n, w/in epsilon, w/confidence

• Uses a greedy algorithm to choose the observation plan

53

In-Net Regression

• Linear regression : simple way to predict future values, identify outliers

• Regression can be across local or remote values, multiple dimensions, or with high degree polynomials– E.g., node A readings vs. node B’s– Or, location (X,Y), versus temperature

E.g., over many nodes

X vs Y w/ Curve Fit

y = 0.9703x - 0.0067

R2 = 0.947

0

2

4

6

8

10

12

1 3 5 7 9Guestrin, Thibaux, Bodik, Paskin, Madden. “Distributed Regression: an Efficient

Framework for Modeling Sensor Network Data .” Under submission.

54

In-Net Regression (Continued)

• Problem: may require data from all sensors to build model

• Solution: partition sensors into overlapping “kernels” that influence each other– Run regression in each kernel

• Requiring just local communication

– Blend data between kernels– Requires some clever matrix manipulation

• End result: regressed model at every node– Useful in failure detection, missing value

estimation

55

Exploiting Correlations in Query Processing

• Simple idea: – Given predicate P(A) over expensive attribute A– Replace it with P’ over cheap attribute A’ such that P’

evaluates to P – Problem: unless A and A’ are perfectly correlated, P’ ≠ P

for all time• So we could incorrectly accept or reject some readings

• Alternative: use correlations to improve selectivity estimates in query optimization– Construct conditional plans that vary predicate order

based on prior observations

56

Exploiting Correlations (Cont.)

• Insight: by observing a (cheap and correlated) variable not involved in the query, it may be possible to improve query performance – Improves estimates of selectivities

• Use conditional plans• Example

Light > 100 Lux

Temp < 20° C

Cost = 100Selectivity = .5


Expected Cost = 150

Light > 100 Lux

Temp < 20° C



Expected Cost = 150

Light > 100 Lux

Temp < 20° C



Expected Cost = 110

Light > 100 Lux

Temp < 20° C



Expected Cost = 110

Time in [6pm, 6am]

T

F

57

In-Network Join Strategies

• Types of joins: – non-sensor -> sensor– sensor -> sensor

• Optimization questions:– Should the join be pushed down?– If so, where should it be placed?– What if a join table exceeds the

memory available on one node?

58

Choosing Where to Place Operators

• Idea : choose a “join node” to run the operator

• Over time, explore other candidate placements– Nodes advertise data rates to their neighbors– Neighbors compute expected cost of running the

join based on these rates– Neighbors advertise costs– Current join node selects a new, lower cost node

Bonfils + Bonnet, Adaptive and Decentralized Operator Placement for In-Network QueryProcessing IPSN 2003.

59

Topics



60

Adaptivity In Sensor Networks

• Queries are long running• Selectivities change

– E.g. night vs day

• Network load and available energy vary• All suggest that some adaptivity is needed

– Of data rates or granularity of aggregation when optimizing for lifetimes

– Of operator orderings or placements when selectivities change (c.f., conditional plans for correlations)

• As far as we know, this is an open problem!

61

Multiple Queries and Work Sharing

• As sensornets evolve, users will run many queries simultaneously– E.g., traffic monitoring

• Likely that queries will be similar– But have different end points, parameters,

etc

• Would like to share processing, routing as much as possible

• But how? Again, an open problem.

62

Concluding Remarks

• Sensor networks are an exciting emerging technology, with a wide variety of applications

• Many research challenges in all areas of computer science– Database community included– Some agreement that a declarative interface is right

• TinyDB and other early work are an important first step

• But there’s lots more to be done!

Documents

Implementation and Research Issues in Query Processing for Wireless Sensor Networks