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Data Quality and Query Cost in Wireless Sensor Networks

David Yates, Erich Nahum, Jim Kurose, and Prashant Shenoy

IEEE PerCom 2008IEEE PerCom 2008

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Papers

Data Quality and Query Cost in Wireless Sensor Networks

IEEE PerSeNS 2007IEEE PerSeNS 2007

Data Quality and Query Cost in Wireless Sensor Networks

IEEE PerCom 2008IEEE PerCom 2008

with analysis of performance trend

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Outline

Introduction Caching and Lookup Policies Data Quality and Query Cost Discussion of Results Performance Trends

when value deviation is most important when end-to-end delay is most important

Conclusion

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Introduction (1/4)

Data-centric WSNs Environmental and infrastructure monitoring Commercial and industrial sensing

Performance Metrics accuracy total system end-to-end delay the quality of the data provided to sensor networks applications

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Introduction (2/4)Sensor Network Deployment Example:Sensor Network Deployment Example:

Sensor Field

Routers and switches Monitoring and control center

What if the gateway is augmented with storage?

Data server / Gateway (and cache)

Data Acquisition and Caching

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Introduction (3/4)Data Server or Gateway with a Cache:Data Server or Gateway with a Cache:

cache hit vs. cache miss

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Introduction (4/4)

system delay the time between a query arriving and corresponding

reply departing from zero for a cache hit

value deviation the unsigned difference between the data value in

and the true value at location i

dS

mQuery

mReply

vD

mReply

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Caching and Lookup PoliciesPrecise Policies and Approximate Policies

Cac

he

Uti

liza

tio

n

FullFull

Not AvailableNot Available

All hitsage threshold parameter: T

All misses

Greedy Policies

Tcache entries are never deleted, updated, or replaced

Greedy age lookups ( )

Greedy distance lookups ( )

Median-of-3 lookups ( )

T

T

T

Precise PoliciesSimple lookups ( ), Piggybacked queries ( )TT

0T

Spatial LocalitySpatial Locality

'iv iv )(' ii lNl

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Data Quality and Query CostQuality Measurement

Data Quality linear combination of normalized system delay and normalized

value deviation : relative importance

A

nQ

cbn e

Ae

AQ

1

11

1

1

Softmax normalization

v

vv

d

dd

D

DDc

S

SSb

stddev

mean

stddev

mean

10 A

Small values indicate better data quality!

Z-score normalization

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Data Quality and Query CostSimulated Changes to the Environment (1/2)

3-dimensional sensor field Rectangular planes on six faces sensors

Four base stations are placed on the X-Y plane These base stations are connected to the gateway server that

has the common cache.

The sensors always communicate with their closest base station.

101010 6 unit

8 unit

4 unit

X

Y

Z

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Data Quality and Query CostSimulated Changes to the Environment (2/2)

One-way communication to and from

minimum cost to query a location: 2 units (query and reply)

maximum delay to query a location: 2 seconds

llocation

unit 1min | 2' lbl CostrpCost

normalization constant distance

second 1max | ' lbl DelayrqDelay

normalization constant distance

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Data Quality and Query CostTrace-driven Changes to the Environment

Intel Lab Dataset 2-dimensional field 54 Mica2Dot sensors light intensity: the most dynamically changing of sensor values

Assume the sensors always communicate with their closest base station.

Sensor Field

Intel Berkeley Research Lab

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Data Quality and Query CostQuery Workload Model (1/2)

Query Workload Model periodic arrival process random arrival process

The superposition of two query processes polling component

slowly scans the sensor field at fixed rate the period of the polling component of the query workload:

random component queries to different locations in the sensor field average query arrival rate of the random component:

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Data Quality and Query CostQuery Workload Model (2/2)

Simulated changes to the environment exponentially distributed inter-arrival times with mean

90 queries per second

Trace-driven changes to the environment

0.9 queries per second

condqueries/se 81/1

seconds 11.111 9 queries/second

seconds 600condqueries/se 81.0

0.09 queries/second

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Discussion of ResultsSimulated Testing Dataset

A. Jindal and K. Psounis Reference:Reference:

Modeling Spatially-correlated Sensor Network Data, SECON 2004

Modeling Spatially Correlated Data in Sensor Networks, TOSN 2006

Download ToolsDownload Tools

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Discussion of ResultsQuery Cost vs. Data Quality Trade-off

Query Cost vs. Data QualityQuery Cost vs. Data Quality

Correlated changes over 1000 locations Trace-driven changes over 54 locations

A = 0.1 A = 0.1

100% cache hit

0% cache hit

linear trade-off

linear trade-off

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Discussion of ResultsQuery Cost vs. End-to-End Delay

Query Cost vs. End-to-End DelayQuery Cost vs. End-to-End Delay

Correlated changes over 1000 locations Trace-driven changes over 54 locations

A = 0.1 A = 0.1

an increase in the normalized delay term!

1.18 4.4

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Discussion of ResultsQuery Cost vs. Data Quality Trade-off

Query Cost vs. Data QualityQuery Cost vs. Data Quality

Correlated changes over 1000 locations Trace-driven changes over 54 locations

A = 0.9 A = 0.9

No trade-off No trade-off

the best performance the best performance

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Discussion of ResultsHit Ratios, Query Costs, and End-to-End Delays

Hit Ratios, Query Costs, and End-to-End DelaysHit Ratios, Query Costs, and End-to-End Delays

Correlated changes over 1000 locations Trace-driven changes over 54 locations, 90 queries/second T = 90, 0.9 queries/second88.8T

Hit ratio Query Cost End-to-End Delay

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Discussion of ResultsQuery Cost vs. Value Deviation

Query Cost vs. Value DeviationQuery Cost vs. Value Deviation

Correlated changes over 1000 locations Trace-driven changes over 54 locations

A = 0.1 A = 0.1

increase the dispersion

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Discussion of ResultsWhether Delay or Value Deviation?

Query Cost vs. Data QualityQuery Cost vs. Data Quality

Correlated changes over 1000 locations Trace-driven changes over 54 locations

A = 0.1 A = 0.1

Quality is more important.

Cost is at a premium.

value deviation is more important than delay

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Discussion of ResultsWhether Delay or Value Deviation?

Query Cost vs. Data QualityQuery Cost vs. Data Quality

value deviation is more important than delay

Correlated changes over 1000 locations Trace-driven changes over 54 locations

A = 0.9 A = 0.9

Getting the fast response time of a cache “hit” is worthwhile!

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Performance TrendsWhen Value Deviation is Most Important

Query Cost vs. Data QualityQuery Cost vs. Data Quality

Correlated changes / sec

A = 0.1 A = 0.1

9 of 1000Correlated changes / sec Correlated changes / sec90 of 1000 900 of 1000

A = 0.1

linear trade-off

The results are robust!

value deviation is more important than delay

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Performance TrendsWhen Value Deviation is Most Important

Value Deviation vs. Data QualityValue Deviation vs. Data Quality

Correlated changes / sec

A = 0.1 A = 0.1

9 of 1000Correlated changes / sec Correlated changes / sec90 of 1000 900 of 1000

A = 0.1

strong positive correlation!

Environment Changes Value Deviation

value deviation is more important than delay

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Performance TrendsWhen Value Deviation is Most Important

Query Cost vs. Data QualityQuery Cost vs. Data Quality

Trace-driven changes

A = 0.1 A = 0.1

90 Queries/secondTrace-driven changes9 Queries/second

A = 0.1

0.9 Queries/secondTrace-driven changes

linear trade-off

value deviation is more important than delay

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Performance TrendsWhen Value Deviation is Most Important

Value Deviation vs. Data QualityValue Deviation vs. Data Quality

Trace-driven changes

A = 0.1 A = 0.1

90 Queries/secondTrace-driven changes9 Queries/second

A = 0.1

0.9 Queries/secondTrace-driven changes

strong positive correlation!

value deviation is more important than delay

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Performance TrendsWhen System Delay is Most Important

Query Cost vs. Data QualityQuery Cost vs. Data Quality

Correlated changes / sec

A = 0.9 A = 0.9

9 of 1000Correlated changes / sec Correlated changes / sec90 of 1000 900 of 1000

A = 0.9

delay is more important than value deviation

the best performance

The results are robust!

No trade-off

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Performance TrendsWhen System Delay is Most Important

End-to-End Delay vs. Data QualityEnd-to-End Delay vs. Data Quality

Correlated changes / sec

A = 0.9 A = 0.9

9 of 1000Correlated changes / sec Correlated changes / sec90 of 1000 900 of 1000

A = 0.9

delay is more important than value deviation

strong positive correlation!

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Performance TrendsWhen System Delay is Most Important

Query Cost vs. Data QualityQuery Cost vs. Data Quality

A = 0.9 A = 0.9 A = 0.9

delay is more important than value deviation

Trace-driven changes90 Queries/second

Trace-driven changes9 Queries/second 0.9 Queries/second

Trace-driven changes

the best performance

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Conclusion We measure the benefit and cost of seven different

caching and lookup policies. when delay drives data quality when value deviation drives data quality

Query Cost vs. Data Quality linear trade-off cost vs. accuracy and/or cost vs. delay are also linear

The performance trends generally remain the same. with the environment changes on query cost and data quality

performance