ECE 695 Project Presentation
Clustering Sensor Network using
Genetic Algorithm
Karthik Raman
Pranav Vaidya
Spring 2006
Outline
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Introduction & Background
Proposed Genetic Algorithm (GA)
Solution
Experiment Setup and Results
Demonstration of Application
Conclusion & Future Work
Introduction & Background
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Sensor Networks
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Popular, wide range of applications
 Military, environment, health
Small, lightweight, battery powered wireless
nodes distributed over large area
large communication distance from nodes to base
station drain energy & reduce network life
Our goal
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Use GA to cluster sensor network to minimize the
total communication distance and prolong the
network life.
Example of Clustered Network
Base
Station
Cluster
Head
Sensors
Clustering the Network
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Partitioning nodes into independent
clusters
Various methods for clustering
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Drawback
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Ex. K–means, Fuzzy c-means clustering
Assume the number of clusters beforehand
Our contribution
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Dynamic Sensor Network
Background on Genetic Algorithm (GA)
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One of the major areas in Evolutionary Computation
(EC)
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EC consists of machine learning optimization and
classification paradigms based on genetics and
natural selection
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GA mimics survival of the fittest strategy in nature by
preferentially selecting a fitter genetic pool so that
future generation will have fitter population members
GA Terminology
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Population: set of points in problem domain, each
member being a potential solution.
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Fitness: A value proportional to the function we
want to optimize
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Generated randomly
Fitness value and fitness function
Selection: selecting a pool of high fitness
population members
GA Operators: mimic reproduction
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Crossover: pass information from one generation to next
to guide population to acceptable solution
Mutation: introduce diversity to tunnel through local
optima
GA Algorithm
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The series of operations carried out when
implementing a canonical GA paradigm are:
1. Initialize the population (randomly),
2. Calculate fitness for each individual in the
population,
3. Reproduce selected individuals to form a new
population,
4. Perform crossover and mutation on the
population and
5. Loop to step 2 until some condition is met.
Proposed GA Solution
Problem Representation
Nodes
Bits
N0
N1
N2
N3
N4
N5
N6
N7
N8
N9
1
0
1
0
0
0
0
0
0
1
Cluster Head
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Cluster Head
Cluster Head
Represent the population member in a binary format
Each bit represents a node
A normal node is represented by a 0 at the specific bit
location
If the node is a cluster head then we have a 1 at the
corresponding bit position
Nodes N0, N2 and N9 are the cluster heads
Nodes N1, N3 – N8 are the normal nodes.
Fitness Function Discussion
To transmit a k-bit message across a distance of
d, the energy consumed can be represented
E(k,d)=Eelec* k + Eamp * k * d2
Where:
 Eelec is the radio energy dissipation
 Eamp is a transmit amplifier energy dissipation
 To receive a k-bit message, the energy consumed
is as follows:
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ERx(k) = Eelec * k
Our Fitness Function
F=w*(D-distancei)+(1-w)*(N-Hi)+α*Battery_State
Where:
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w is the biasing factor;
D is the total distance of all nodes to the sink;
Distancei is the sum of the distance from regular
nodes to cluster heads plus the sum of the
distances fro all cluster heads to the sink;
Hi is the number of cluster heads;
N is the total number of nodes;
α is weighting factor for Battery_State;
Battery_State is a measure of current battery life;
Selection Method-Roulette Wheel
Section
Roulette Wheel Selection
10%
30%
20%
7%
33%
GA Operators-Crossover
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One-Point Crossover
Before Crossover:
Indv1:
1
1
1
0
0
1
0
1
Indv2:
1
0
1
1
1
1
1
0
Crossover Point
After Crossover:
Child1:
1
1
1
0
1
1
1
0
Child2:
1
0
1
1
0
1
0
1
GA Operators-Mutation
Before Mutation:
Indv:
1
1
1
1
1
1
0
1
1
1
1
0
1
1
1
1
After Mutation:
Indv:
Experiment Setup and Results
Application Demo
Conclusion & Future Work
Experiment Setup and Results
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Simulation Test Bed
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C# and .Net 1.0 Framework
Experiment Setup and Results
Description of Experiment
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5 random deployment scenarios using the
simulation test bed
100 sensor nodes and data collector
performed clustering using GA and analyzed the
results against the criteria listed below
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Performance of GA to maximize distance savings
Performance of GA to minimize number of cluster heads
Performance of GA to minimize energy dissipation in
overall network
Results
Performance of GA to maximize distance savings
Distance Saved V/S Generations
15000
14500
14000
Distance Saved
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13500
Distance Saved
13000
12500
12000
11500
1
8
15
22
29
36
43
50
57
Generations
64
71
78
85
92
99
Results..
Performance of GA to minimize number of cluster heads
No Of Cluster Heads V/S Generations
No Of Cluster Heads
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40
35
30
25
20
15
No Of Cluster Heads
10
5
0
1
8 15 22 29 36 43 50 57 64 71 78 85 92 99
Generations
Results..
Performance of GA to minimize energy dissipation in overall network
First Random Walk
Energy Dissipation
1.2
1
Normalized Energy
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0.8
Normalized Energy
Without Clustering
0.6
Normalized Energy
With Clustering
0.4
0.2
0
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29
Epoch
Results..
Second Random Walk
Normalized Energy V/S Epoch
1.4
Normalized Energy
1.2
1
Normalized Energyy
Without Clustering
0.8
Normalized
Energy+Sheet2!$1:$1
With Clustering
0.6
0.4
0.2
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43
Epoch
Results..
Third Random Walk
Normaliz ed Energy V/S Epoch
1.2
Normalized Energy
1
0.8
Normalized Energy
Without Clustering
0.6
Normalized Energy
With Clustering
0.4
0.2
0
1
4
7
10
13
16
19
Epoch
22
25
28
31
Results…
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Summary
Scenario
performance
% cases
performance of
order 2
1st random walk
> order 2
99%
2nd random walk
> order 2
90%
3rd random walk
> order 2
99%
Application Demo
Conclusion & Future Work
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Our application provides a GA based
method to reduce the communication
distance in sensor networks via clustering.
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We have shown successfully that our
algorithm performs better to the order of
2 in almost 99% of the cases.
Conclusion & Future Work
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Extending the simulation test bed to use other mobility
models.
Evaluation of clustering algorithm using Linear Vector
Quantization (LVQ) and Particle Swarm Optimization (PSO)
and comparison with GA
The fitness function can be based on a lot of other
optimization parameters namely battery charge and
discharge of the nodes.
routing protocol for the setup, steady state and tear down
phase for the sensor networks with cluster head authorization
from data collector, cluster head advertisement and fault
tolerance techniques.
REFERENCES
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