ECE 695 Project Presentation

Clustering Sensor Network using Genetic Algorithm

Karthik Raman

Pranav Vaidya

Spring 2006

Outline

Introduction & Background Proposed Genetic Algorithm (GA)

Solution Experiment Setup and Results Demonstration of Application Conclusion & Future Work

Introduction & Background

Sensor Networks 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

Use GA to cluster sensor network to minimize the total communication distance and prolong the network life.

Cluster Head

Base Station

Sensors

Example of Clustered Network

Clustering the Network

Partitioning nodes into independent clusters

Various methods for clustering Ex. K–means, Fuzzy c-means clustering

Drawback Assume the number of clusters beforehand

Our contribution Dynamic Sensor Network

Background on Genetic Algorithm (GA)

One of the major areas in Evolutionary Computation (EC)

EC consists of machine learning optimization and classification paradigms based on genetics and natural selection

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 Population: set of points in problem domain, each

member being a potential solution. Generated randomly

Fitness: A value proportional to the function we want to optimize Fitness value and fitness function

Selection: selecting a pool of high fitness population members

GA Operators: mimic reproduction Crossover: pass information from one generation to next

to guide population to acceptable solution Mutation: introduce diversity to tunnel through local

optima

GA Algorithm 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 and5. Loop to step 2 until some condition is met.

Proposed GA SolutionProblem Representation

Nodes N0 N1 N2 N3 N4 N5 N6 N7 N8 N9

Bits 1 0 1 0 0 0 0 0 0 1

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.

Cluster Head Cluster Head Cluster Head

Fitness Function Discussion To transmit a k-bit message across a distance of

d, the energy consumed can be representedE(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: ERx(k) = Eelec * k

Our Fitness FunctionF=w*(D-distancei)+(1-w)*(N-Hi)+α*Battery_State

Where: 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%

20%

33%

7%

30%

GA Operators-Crossover

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

After Mutation:

Indv: 1 1 1 0 1 1 1 1

Experiment Setup and Results

Application Demo

Conclusion & Future Work

Experiment Setup and Results Simulation Test Bed

C# and .Net 1.0 Framework

Experiment Setup and Results

Description of Experiment 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 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

11500

12000

12500

13000

13500

14000

14500

15000

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99

Generations

Dis

tan

ce S

aved

Distance Saved

Results.. Performance of GA to minimize number of cluster heads

No Of Cluster Heads V/S Generations

0

5

10

15

20

25

30

35

40

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99

Generations

No

Of C

lust

er H

eads

No Of Cluster Heads

Results.. Performance of GA to minimize energy dissipation in overall network

First Random Walk

Energy Dissipation

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Epoch

No

rm

ali

zed

En

erg

y

Normalized EnergyWithout Clustering

Normalized EnergyWith Clustering

Results..Second Random Walk

Normalized Energy V/S Epoch

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43

Epoch

No

rmali

zed

En

erg

y

Normalized EnergyyWithout Clustering

NormalizedEnergy+Sheet2!$1:$1With Clustering

Results..Third Random Walk

Normalized Energy V/S Epoch

0

0.2

0.4

0.6

0.8

1

1.2

1 4 7 10 13 16 19 22 25 28 31

Epoch

No

rm

ali

zed

En

erg

y

Normalized EnergyWithout Clustering

Normalized EnergyWith Clustering

Results… 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 Our application provides a GA based

method to reduce the communication distance in sensor networks via clustering.

We have shown successfully that our algorithm performs better to the order of 2 in almost 99% of the cases.

Conclusion & Future Work 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.

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