SunHee Yoon and Cyrus Shahabi University of Southern California

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The Clustered AGgregation Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks

SunHee Yoon and Cyrus Shahabi

University of Southern California

ACM Transactions on Sensor Networks 2007ACM Transactions on Sensor Networks 2007

2

Overview

Exploiting Spatial Correlation Towards an Energy Efficient Clustered AGgregation Technique (CAG)

IEEE International Conference on Communications 2005

The Clustered AGgregation (CAG) Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks

ACM Transactions on Sensor Networks 2007

Addition: Contour Maps: Monitoring and Diagnosis in Sensor Networks

The International Journal of Computer and Telecommunications Networking, 2006

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Outline

Introduction The CAG algorithm Measurement and Correlation Model Experimental Study Conclusion and Future Work

additional Contour Maps vs. CAG

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Introduction (1/3)

Efficient in-network aggregation In-network query processing and data aggregation save energy, and reduce computation monitoring applications of WSNs

Tobler’s first law of geography Everything is related to everything else, but near thing

s are more related than distant things. 每件事物彼此之間都會有相關，但是距離近的事物會比遠的事物相關性更高。

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Introduction (2/3)

CAG Clustered AGgregation algorithm leveraging spatial and temporal correlation forms clusters of sensor nodes transmits a single value per cluster similar to Tiny AGgregation (TAG)

CAG can achieve energy-savings. reduce the number of transmissions incur a small error in the query result

TAG: Tiny AGgregation service for ad-hoc sensor networks, OSDI, 2002

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Introduction (3/3)

User-provided error threshold: accuracy requirement parameter bound the difference among the sensor readings in a

cluster approximate answer always stays within the error

threshold of the correct answer

Two phases Query and Response

Two modes Interactive Mode, Streaming Mode

τ

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The CAG algorithmTwo Modes of CAG Operation

Interactive mode Streaming mode

responding with a single value! periodic response messages

Exploits only the spatial correlation.

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The CAG algorithmInteractive Mode

User Query

Query Packet

Query Packet

Query Packet

Base Station

, τQueryID, Oi

τ : user-provided error threshold

CR : Cluster-head sensor Reading

MR

MR

MR

: My local sensor Reading

CRlevelID, MyIDUQ, Parent ,,

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CR τRangeCR τRangeCR

Only cluster-head transmits its sensing value!

? ?

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Example: AVG (43, 59) = ?51AVG (30, 64) = ?47AVG (51, 47, 50) = ?49.5

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ProblemsProblems Cannot provide the results with the bounded error. duplicate sensitive vs. duplicate insensitive Does not consider the size of cluster. Cannot take advantage of the temporal correlation.

Properties of Aggregate Operators

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The CAG algorithmStreaming Mode

A single query generates periodic responses from the network. spatial correlation and temporal correlation epoch duration: i To generate a query reply every i seconds.

Compare to the Interactive Mode periodic response vs. one-shot response The clusters need to be updated and repaired. allow cluster size estimation

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The CAG algorithmStreaming Mode: Cluster Adjustment

Cluster Adjustment Interval…

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The CAG algorithmStreaming Mode: Cluster Adjustment

Adjustment cost can be controlled. The cluster adjustment messages only propagate to the

nodes within the same cluster. The parent node always performs cluster adjustment

before its children.

Cluster Adjustment IntervalCluster Adjustment Interval maximum amount of time that data can be out-of-range A smaller interval makes system more responsive to the

data dynamics.

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The CAG algorithmStreaming Mode: Cluster Size Estimation

Errors in the result obtained from the interactive mode can be large. Equal weights are assigned to clusters of different size. Estimate the size of the cluster. Too costly in the interactive mode, but practical in the

streaming mode.

With high temporal and spatial correlations… Cluster adjustment is infrequent. Cluster size estimation overhead is small.

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The CAG algorithmStreaming Mode: Cluster Size Estimation

Example:

count increment message

Count the number of count increment messages…

cluster adjustment!

count decrement message

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Measurement and Correlation ModelVariogram Models

The variogram is defined as follows:

h: distance X(p) and X(p+h): values of each pair of points at distance h

Three common variogram models Spherical Model Linear Model Fractal Model

]))()([2

1)( 2hpXp(XEhγ

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Measurement and Correlation ModelVariogram Models Spherical ModelSpherical Model

increases linearly in the beginning, then becomes a sill Data is correlated over a shorter distance than others.

Linear ModelLinear Model Data becomes less correlated as distance increases.

Fractal ModelFractal Model ubiquitous in nature should be linear in a graph of against))(log( h )log(h

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Measurement and Correlation ModelData Sets

Sensor Data Measurement in a Regular Grid two different environments light and temperature mica2 motes and MTS 300 sensor boards

Outdoor: Exposition Park in L.A. Indoor: 4th floor of Tutor Hall at USC

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Data with Irregular Mote Placement Great Duck Island humidity, temperature, light, and pressure Irregular inter-node distance are subdivided into a number

of intervals called logs.

Sensor Deployment

Map of Great Duck Island

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Synthetic Data from the Statistical Model Reference: Modeling Spatially-correlated Sensor Network

Data, SECON, 2004 250m x 250m grid Parameters: larger h results in higher spatial correlation

7 ,5 ,3 ,1 and , ,2/1 hi

7h data 9h data

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Synthetic Data from the Ecological Model 250m x 250m grid Similar to the fractal pattern found in the environment. Fractal Dimension = 2 Spatial Pattern Data

High correlation level:

between 7h and 9h

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Measurement and Correlation ModelThe Spatial Data Model

Apply temperature data to the correlation model.

Linear modelLinear model:

Spherical modelSpherical model:

Quasi-spherical modelQuasi-spherical model:

hh 81

425)(

otherwise ,0

36for ,1254

360for ,362

1

362

31254

)(

3

h

hhh

h

5942076.80590610089.0)( 23 .hh.- hh

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Measurement and Correlation ModelThe Spatial Data Model

Variogram

Temperature from Exposition Park Light from Exposition Park

linear functionality


spherical characteristic

Temperature data from Exposition Park


spherical characteristic

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Experimental StudyEvaluation Metrics and Experimental Setup

Reduced number of transmissionsReduced number of transmissions interactive mode:

in the streaming mode

Accuracy of resultAccuracy of result

%100)(

)()( #

TAGnTX

CAGnTXTAGnTXonstransmissiofreduced

)( CAGnTXonstransmissiofnumber

%MinValueMaxValue

sultReCorrectsultReEstimatederror absolute 100

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Experimental StudyEvaluation Metrics and Experimental Setup

TOSSIM simulator of TinyOS 1.1.8 Temperature Data

1, 4, 6, and 7 PM from Exposition Park Temperature readings from Great Duck Island

Three different deployment densities. 250m x 250m grid dense: 550 nodes (26 neighbors per node) moderate: 375 nodes (17 neighbors per node) sparse: 200 nodes (9 neighbors per node)

Two types of topology. lossless and empirical loss rates = 0, 2, 4, 10, and 20%τ

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Experimental StudyExperimental Results: Interactive Mode

375 nodes 250m x 250m = 20% 9h synthetic data

τ

Root Node

Most new clusters are built along the diagonal band!

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Improved performance of CAG compared to TAG

37.5%

51.25%

Test Data: Temperature from Exposition Park

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Precision with empirical radio profileTest Data: Temperature from Exposition Park

Error out-of-bound

9.375%

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Precision with perfect link reliability

The temperature data in the physical world follows the normal distribution.

Test Data: Temperature from Exposition Park

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Experimental StudyExperimental Results: Streaming Mode

Three different data sets for measurement study. Great Duck Island DatasetGreat Duck Island Dataset

35 nodes temperature data (recorded once per hour) four consecutive days

Stair-wise DatasetStair-wise Dataset from Exposition Park temperature readings between 4 PM and 6 PM

Linear DatasetLinear Dataset from Exposition Park temperature snapshots at 4 PM and 6 PM Generate a linear dataset by linearly interpolated.

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Total transmissions overhead between TAG and CAG

63.07% reduction

70.24% reduction

Test Data: Temperature snapshot from Exposition Park

(acc

umul

ated

)

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Total transmissions overhead between TAG and CAGTest Data: Temperature from Great Duck Island

19.0% reduction

Less nodes send their responses to the root!

(acc

umul

ated

)

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Cluster adjustment overhead: Query Flooding and CAGTest Data: Temperature from Exposition Park (Linear Data Set)

52

20000

Both algorithms are reclustering at the same frequency!

(acc

umul

ated

)

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Cluster adjustment overhead: Query Flooding and CAGTest Data: Temperature from Great Duck Island

6650

249

unacceptable overhead…

local repair vs. global adjustment

(acc

umul

ated

)

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Cluster adjustment overhead: Linear and Stair-wiseTest Data: Temperature snapshot from Exposition Park

cluster adjustment

Cluster adjustment is continuous!

(acc

umul

ated

)

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Breakdown of transmission overheadTest Data: Temperature from Exposition Park (Linear Data Set)

987

76

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Breakdown of transmission overheadTest Data: Temperature from Great Duck Island

1531

229

flat decrease vs. gradual decrease

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The accuracy of result achievedTest Data: Temperature from Exposition Park (Linear Data Set)

%20τ

3.09%

downward trend!

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The accuracy of result achievedTest Data: Temperature from Great Duck Island

%20τ

6.26%

Error out-of-bound!

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Conclusion and Future Work

Clustered AGgregation Technique energy-efficient in-network aggregation leveraging both spatial and temporal correlations resilient to the packet loss ensure bounded approximation

We would like to extend this work. hybrid clustering protocol Provide proactive and reactive data acquisition.

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additional. Contour Maps vs. CAG

Sensor nodes that actually sent out reports.

CAG 050.τ Contour Maps

not sufficiently sampled! more evenly sampled!

Contour Maps: Monitoring and Diagnosis in Sensor Networks

Documents

SunHee Yoon and Cyrus Shahabi University of Southern California