TASC: Topology Adaptive Spatial Clustering for Sensor Networks

Reino Virrankoski, Dimitrios Lymberopoulos and Andreas SavvidesEmbedded Networks and Application Lab Electrical Engineering Department Yale University, New Haven

Infocom 2005

Ju-Mei Li

Outline

Introduction TASC

Distributed Leader Election Discovering Local Network Structure

Weight computation Grouping Similar Densities

Density reachability

Evaluation Conclusion

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Introduction

A good topology of large-scale sensor networks should help Sensor nodes coordination Network management Data aggregation and compression

Goal Through the development of weights and dynamic dens

ity reachablility Topology Adaptive Spatial Clustering Scheme (TASC)

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TASC: Distributed Leader Election

Input information 2-hop neighborhood Inter-node distance measurements Min. cluster size MinPoints

Each node uses input information to compute Weight Number of density reachable node

Midmost position on each shortest path, biggest weight

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TASC: Distributed Leader Election

f

g

b

a

c

e

h

d

i

k

j

BroadcastToNeighborhood(weight)BroadcastToNeighborhood(weight)

Select the heaviest density reachable node as nominee

BroadcastToNeighborhood(nominee)

Select the heaviest density reachable node as nominee

BroadcastToNeighborhood(nominee)

Density reachable nodes of node i = 4

Density reachable nodes of node j = 7

Density reachable nodes of node k = 3

Select the closest nominee as leader

BroadcastToNeighborhood(leaderID, nodeID)

Select the closest nominee as leader

BroadcastToNeighborhood(leaderID, nodeID)

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TASC: Distributed Leader Election

f

g

b

a

c

e

h

d

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j

If this node is leader

until election timeout;

BroadcastToNeighborhood(clustermenbers)

If this node is leader

until election timeout;

BroadcastToNeighborhood(clustermenbers)

If clustersize is received

If clustersize < min. cluster size = 4

select the closest neighbor for which

clustersize ≥ min. cluster size = 4

and joints its cluster

BroadcastToNeighborhood(leaderID, clustersize)

If clustersize is received

If clustersize < min. cluster size = 4

select the closest neighbor for which

clustersize ≥ min. cluster size = 4

and joints its cluster

BroadcastToNeighborhood(leaderID, clustersize)

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TASC: Weight computation

A B C D E

4 7 7 48

A-B

A-B-C

A-B-C-D

A-B-C-D-E

B-C

B-C-D

B-C-D-E

C-D

C-D-E

D-E

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TASC: Weight computation

Including distance in Weight Computation If node k is

found on path from node i to node j

in between node a and node b Then the weight increment of

node k is given

3

2

12

53

,

,,

GA

EDDA

l

llD node of weight

A

B

C

D E G

F

H

3 45

1.29 10.15 11.46 1

0.86

0.49

0.84 0.51

4

3

12

45

,

,,

GA

GEED

l

llE node of weight

ij

bkkaij l

llw ,,

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TASC: Density reachability

i

Sensing range <= transmission range

If MinPoints = m = 3

ri

Could be large, equal, or small than sensing range

Could be large, equal, or small than sensing range

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TASC: Density reachability

i

a

b

c

jk

d

e

Density reachable nodes of node i : node j, node k, node a, node b, and node c

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TASC: Density reachability

i

k

j

i

k

j

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TASC: Distributed Leader Election

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Evaluation

PARSEC 100 random scenarios 100 nodes are deployed on 1000*1000 Measurement range

200, 250, 300, 350, 400 Minimum cluster size: 4 Shortest path is done on each node

Floyd-Warshall algorithm

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Evaluation

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Evaluation

Measurement range: (a)200, (b)300

(a) (b)

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Evaluation Measurement range: (a)200, (b)300, (c)400

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Evaluation

MinPoints = 2 MinPoints = 4

MinPoints = 6

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Conclusion

This paper proposed a TASC algorithmWhich uses

Weight Number of density reachable node

To decompose large network into smaller locally clusters

Thank You!!

Ju-Mei Li

TASC: Density reachability

i

k

j

i

j

k