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ECE 695 Project Presentation Clustering Sensor Network using Genetic Algorithm

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Small, lightweight, battery powered wireless nodes distributed over large area ... a fitter genetic pool so that future generation will have fitter population members ... – PowerPoint PPT presentation

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Title: ECE 695 Project Presentation Clustering Sensor Network using Genetic Algorithm


1
ECE 695 Project PresentationClustering Sensor
Network using Genetic Algorithm
  • Karthik Raman
  • Pranav Vaidya

Spring 2006
2
Outline
  • Introduction Background
  • Proposed Genetic Algorithm (GA) Solution
  • Experiment Setup and Results
  • Demonstration of Application
  • Conclusion Future Work

3
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.

4
Example of Clustered Network
5
Clustering the Network
  • Partitioning nodes into independent clusters
  • Various methods for clustering
  • Ex. Kmeans, Fuzzy c-means clustering
  • Drawback
  • Assume the number of clusters beforehand
  • Our contribution
  • Dynamic Sensor Network

6
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

7
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

8
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 and
  • 5. Loop to step 2 until some condition is met.

9
Proposed GA SolutionProblem Representation
Cluster Head
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.

10
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
  • ERx(k) Eelec k

11
Our Fitness Function
  • Fw(D-distancei)(1-w)(N-Hi)aBattery_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
  • a is weighting factor for Battery_State
  • Battery_State is a measure of current battery
    life

12
Selection Method-Roulette Wheel Section
13
GA Operators-Crossover
  • One-Point Crossover

Before Crossover
Crossover Point After
Crossover
14
GA Operators-Mutation
Before Mutation
After Mutation
15
Experiment Setup and ResultsApplication
DemoConclusion Future Work
16
Experiment Setup and Results
  • Simulation Test Bed
  • C and .Net 1.0 Framework

17
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

18
Results
  • Performance of GA to maximize distance savings

19
Results..
  • Performance of GA to minimize number of cluster
    heads

20
Results..
  • Performance of GA to minimize energy dissipation
    in overall network
  • First Random Walk

21
Results..
  • Second Random Walk

22
Results..
  • Third Random Walk

23
Results
  • Summary

24
Application Demo
25
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.

26
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.

27
REFERENCES
  • 1 W. R. Heinzelman, A. Chandrakasan, and H.
    Balakrishnan. Energy-Efficient Communication
    Protocol for Wireless Micro-sensor Networks. In
    Proceedings of the Hawaii International
    Conference on System Science, Maui, Hawaii, 2000.
  • 2 Selim, S. Z. and Ismail, M. A. K-means type
    algorithms A generalized convergence theorem and
    characterization of local optimality. IEEE Trans.
    Pattern Anal. Mach. Intell. 6, 8187, 1984.
  • 3 Russell C. Eberhart and Yuhui Shi
    Computational Intelligence Concepts to
    Implementations. Indiana
  • 4 J. C. Bezdek (1981) "Pattern Recognition
    with Fuzzy Objective Function Algoritms", Plenum
    Press, New York, http//www.elet.polimi.it/upload/
    matteucc/Clustering/tutorial_html/cmeans.html
  • 5 Tracy Camp, Jeff Boleng and Vanessa Davies
    A Survey of Mobility Models for Ad Hoc Network
    Research, Golden, CO, 2002
  • 6 Seapahn Meguerdichian, Farinaz Koushanfar,
    Miodrag Potkonjak and Mani B. Srivastava
    Coverage Problems in Wireless Ad-hoc Sensor
    Networks, Los Angeles, CA, 2001
  • 7 F. L. LEWIS Wireless Sensor Networks, Ft.
    Worth, Texas, 2004
  • 8 Jason Lester Hill System Architecture for
    Wireless Sensor Networks, University of
    California, Berkeley, 2000
  • 9 Silvia Nittel, Kelvin T. Leung, Amy
    Braverman Scaling Clustering Algorithms for
    Massive Data Sets using Data Streams, Los
    Angeles, CA, March 2004
  • 10 Xiaohui Cui, Thomas E. Potok and Paul
    Palathingal Document Clustering using Particle
    Swarm Optimization, Oak Ridge, TN, 2005
  • 11 Wendi Heinzelman, Anantha Chandrakasan and
    Hari Balakrishnan Energy-efficient
    Communication Protocols for Wireless Microsensor
    Networks, Maui, HI, January 2000
  • 12 A. Bruce McDonald and Taieb F. Znati A
    Mobility-Based Framework for Adaptive Clustering
    in Wireless Ad Hoc Networks, 1999
  • 13 Guolong Lin, Guevara Noubir and Rajmohan
    Rajaraman Mobility Models for Ad Hoc Network
    Simulation, Boston, MA, 2004
  • 14 Greg Badros Evolving Solutions An
    Introduction to Genetic Algorithms,
    http//www.duke.edu/vertices/update/win95/genalg.h
    tml, 1995
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