An Energy-Efficient Voting-Based Clustering Algorithm for

1
An Energy-Efficient Voting-Based Clustering
Algorithm for Sensor Networks
Min Qin and Roger Zimmermann Computer Science
Department, Integrated Media Systems Center
5/24/2005
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An Energy-Efficient Voting-Based Clustering
Algorithm for Sensor Networks

• Presentation outline
• Clustering in sensor networks
• Issues of traditional clustering protocols
• Voting-based clustering algorithm (VCA)
• Performance evaluation
• Conclusions

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Clustering in sensor networks

• The network is divided into many clusters
• Each cluster has one cluster head
• Data collected from sensors are sent to the
  cluster head first, and then forwarded to the
  sink
• Cluster head is capable of aggregating data from
  all sensors

4
A sample cluster
Cluster head
sink
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Issues of traditional clustering protocols

• Based on local weights or probabilities
• Often result in undesirable cluster formations
• Many of them do not consider topology information
  
• Load balancing is hard

6
Example a network with 4 sensors
Desirable cluster formation
C
D
B
A
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Clustering by traditional algorithms (HEED)
C
0.6
D
0.7
B
A
0.9
1
8
Resulting cluster formation (HEED)
C
D
B
A
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Voting-based clustering algorithm (VCA)

• Main ideas
• A sensors importance should be reflected from
  all its neighbors (including itself) rather than
  from itself
• Use voting to reflect the importance of different
  neighbors
• Topology and residual energy are two primary
  factors in selecting cluster heads
• Assumptions about sensors
• Energy-aware
• Quasi-stationary

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VCA An example
D vote for all its neighbors (including itself)
C
0.25
0.25
D
0.25
0.25
B
A
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VCA An example
D collect votes from all its neighbors
0. 5
C
0.5
D
D
0.5
0.5
B
A
0.5
0.5
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VCA An example
Each node calculates the total vote it has got
C
0.75
1.75
D
B
0.75
A
0.75
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Rules for voting

• The sum of the votes a node gives to all its
  neighbors (including itself) is 1
• A neighbor with high residual energy should get
  more votes than a neighbor with low residual
  energy

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Load balancing in VCA

• If a sensor is covered by multiple cluster heads,
  the following two load balancing strategies are
  used
• Node degree
• Join the head with the minimum node degree
• Balance the size of all clusters
• Fitness
• Join the cluster head with the highest fitness
• Balance energy distribution of cluster heads

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Procedures of VCA
1.Each sensor calculates its votes to all its
neighbors 2.Sensors calculate and broadcast the
total vote they have got from their neighbors.
3.Cluster heads are elected from those nodes
that have the highest votes in their
neighborhood 4. Sensors that are covered by at
least one cluster head withdraw from voting 5.
the remaining sensors restart from step 2 by
ignoring the votes from those sensors that have
withdrawn from the voting
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Head Election in Multiple Rounds
In the 1st round
0. 5
C
0.5
0.25
0.25
D
D
0.25
E
0.25
0.5
0.5
0.5
0.33
0.33
B
A
0.5
0.33
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Head Election in Multiple Rounds
After the 1st round
0. 75
C
1.58
D
D
E
0.83
B
A
0.75
1.08
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Head Election in Multiple Rounds
In the 2nd round, E ignores vote from A
C
D
D
E
0.5
0.5
B
0.33
A
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Head Election in Multiple Rounds
After the 2nd round
0. 75
C
A is covered by 2 cluster heads, it chooses E
since it has lower degree
1.58
D
D
E
0.5
B
A
0.75
1.08
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Properties of VCA

• Message complexity O(N)
• Time complexityO(N)
• Normally finishes within 2-5 iterations
• Cluster heads are well distributed, No two
  cluster heads covers each other
• High-degree nodes tend to get more votes, and
  give less votes to others

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Simulation settings
Parameter Value
S 0, 1002
Sink location (50,200)
Cluster radius 15 m
Data packet 250 bytes
Clustering packet 30 bytes
WITHDRAW packet 10 bytes
Network operation phase 5 TDMA frames
Energy for data fusion 5nJ/bit/signal
Initial Energy 2J
Threshold distance (d0) 100 m
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Network lifetime (when the first node dies)

• A sensor joins the cluster head with the highest
  fitness value in VCA-fitness
• A sensors joins the cluster head with the minimum
  node degree in VCA-Min degree
• Average result from 100 independent simulations

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Network lifetime (when the last node dies)
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Conclusions

• VCA is completely distributed, energy-efficient
  and location unaware
• Using fitness can balance the energy across the
  network, sensors tend to die at a similar time
• Using Min-degree can balance the size of all
  clusters, some nodes may live much longer than
  others
• Democracy can be very helpful for sensor
  networks.

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Conclusions

• Questions?
• Comments?
• http//dmrl.usc.edu/publications/

Thank you!