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GLOBECOM 2013. A Multi-Objective Genetic Algorithm for Constructing Load-Balanced Virtual Backbones in Probabilistic Wireless Sensor Networks. Jing (Selena) He Department of Computer Science, Kennesaw State University Shouling Ji and Raheem Beyah

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slide1

GLOBECOM 2013

A Multi-Objective Genetic Algorithm forConstructing Load-Balanced Virtual Backbones inProbabilistic Wireless Sensor Networks

Jing (Selena) He

Department of Computer Science, Kennesaw State University

Shouling Ji and Raheem Beyah

School of Electrical and Computer Engineering, Georgia Institute of Technology

Yingshu Li

Department of Compute Science, Georgia State University

outline
Outline
  • Motivation
  • Problem Definition
  • Multi-Objective Genetic Algorithm (MOGA)
  • Performance Evaluation
  • Conclusion
outline1
Outline
  • Motivation
  • Problem Definition
  • Multi-Objective Genetic Algorithm (MOGA)
  • Performance Evaluation
  • Conclusion
dominator partition

Motivation

Dominator Partition

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2

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6

7

8

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Imbalanced Dominator Partition

Balanced Dominator Partition

our contributions

Motivation

Our Contributions
  • Highlight the use of lossy links when constructing Virtual Backbone (VB) for Probabilistic WSNs
  • Propose new optimization problem called LBVBP
    • LBVB construction problem under PNM
  • Propose a MOGA to solve LBVBP
  • Conduct simulations to validate the proposed algorithm
outline2
Outline
  • Motivation
  • Problem Definition
  • Multi-Objective Genetic Algorithm (MOGA)
  • Performance Evaluation
  • Conclusion
lbvb in probabilistic wsns

Problem Definition

LBVB in Probabilistic WSNs

Actual Traffic Load

Potential Traffic Load

  • Objectives:
    • Minimum-sized VB
    • Minimize VB p-norm
    • Minimize Allocation p-norm
  • MOGAs are very attractive to solve MOPs, because they have the ability to search partially ordered spaces for several alternative trade-offs. Additionally, an MOGA can track several solutions simultaneously via its population.

VB p-norm = 5.89

Allocation p-norm = 3.53

VB p-norm = 8.29

Allocation p-norm = 4.19

outline3
Outline
  • Motivation
  • Problem Definition
  • Multi-Objective Genetic Algorithm (MOGA)
  • Performance Evaluation
  • Conclusion
fitness vector

MOGA

Fitness Vector

Minimize Allocation p-norm

Minimize VB p-norm

Minimize size

genetic operations

MOGA

Genetic Operations
  • Crossover: exchange part of genes
  • Mutation: flip the gene values
  • Dominatee Mutation:
algorithm

MOGA

Algorithm

Return the fittest

Replacement

Selection

Population Initialization

Recombination

Evaluation Process

outline4
Outline
  • Motivation
  • Problem Definition
  • Multi-Objective Genetic Algorithm (MOGA)
  • Performance Evaluation
  • Conclusion
slide18

Performance Evaluation

Simulation Results

Our method

  • MOGA prolong network lifetime by 25% on average compared with MCDS
  • MOGA prolong network lifetime by 6%on average compared with GA

Others’ Methods

outline5
Outline
  • Motivation
  • Problem Definition
  • Multi-Objective Genetic Algorithm (MOGA)
  • Performance Evaluation
  • Conclusion
conclusion

Conclusion

Conclusion
  • Address the problem of construction a load-balanced VB in a probabilistic WSN (LBVBP), which to assure that the workload among each dominator is balanced
  • Propose an effective MPGA algorithm to solve LBVBP
  • Simulation results demonstrate that using an LBVB can extend network lifetime significantly
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