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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
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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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The CAG algorithmInteractive Mode
CR τRangeCR τRangeCR
Only cluster-head transmits its sensing value!
? ?
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The CAG algorithmInteractive Mode
Example: AVG (43, 59) = ?51AVG (30, 64) = ?47AVG (51, 47, 50) = ?49.5
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The CAG algorithmInteractive Mode
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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Measurement and Correlation ModelData Sets
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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Measurement and Correlation ModelData Sets
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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Measurement and Correlation ModelData Sets
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
linear functionality
spherical characteristic
Temperature data from Exposition Park
linear functionality
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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Experimental StudyExperimental Results: Interactive Mode
Improved performance of CAG compared to TAG
37.5%
51.25%
Test Data: Temperature from Exposition Park
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Experimental StudyExperimental Results: Interactive Mode
Precision with empirical radio profileTest Data: Temperature from Exposition Park
Error out-of-bound
9.375%
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Experimental StudyExperimental Results: Interactive Mode
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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Experimental StudyExperimental Results: Streaming Mode
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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Experimental StudyExperimental Results: Streaming Mode
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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Experimental StudyExperimental Results: Streaming Mode
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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Experimental StudyExperimental Results: Streaming Mode
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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Experimental StudyExperimental Results: Streaming Mode
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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Experimental StudyExperimental Results: Streaming Mode
Breakdown of transmission overheadTest Data: Temperature from Exposition Park (Linear Data Set)
987
76
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Experimental StudyExperimental Results: Streaming Mode
Breakdown of transmission overheadTest Data: Temperature from Great Duck Island
1531
229
flat decrease vs. gradual decrease
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Experimental StudyExperimental Results: Streaming Mode
The accuracy of result achievedTest Data: Temperature from Exposition Park (Linear Data Set)
%20τ
3.09%
downward trend!
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Experimental StudyExperimental Results: Streaming Mode
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