GLOBECOM 2013
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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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Jing (Selena) He Department of Computer Science, Kennesaw State University

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Jing selena he department of computer science kennesaw state university

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


Load balanced virtual backbone lbvb

Motivation

Load-Balanced Virtual Backbone (LBVB)

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LBVB

MCDS


Dominator partition

Motivation

Dominator Partition

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Imbalanced Dominator Partition

Balanced Dominator Partition


Transitional region phenomenon

Motivation

Transitional Region Phenomenon


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


Moga overview

MOGA

MOGA Overview


Chromosomes

MOGA

Chromosomes


Fitness vector

MOGA

Fitness Vector

Minimize Allocation p-norm

Minimize VB p-norm

Minimize size


Dominating tree

MOGA

Dominating Tree


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


Jing selena he department of computer science kennesaw state university

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


Jing selena he department of computer science kennesaw state university

Q & A


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