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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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

  • Motivation

  • Problem Definition

  • Multi-Objective Genetic Algorithm (MOGA)

  • Performance Evaluation

  • Conclusion


Outline

  • Motivation

  • Problem Definition

  • Multi-Objective Genetic Algorithm (MOGA)

  • Performance Evaluation

  • Conclusion


Motivation

Load-Balanced Virtual Backbone (LBVB)

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LBVB

MCDS


Motivation

Dominator Partition

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Imbalanced Dominator Partition

Balanced Dominator Partition


Motivation

Transitional Region Phenomenon


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


Outline

  • Motivation

  • Problem Definition

  • Multi-Objective Genetic Algorithm (MOGA)

  • Performance Evaluation

  • Conclusion


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


Outline

  • Motivation

  • Problem Definition

  • Multi-Objective Genetic Algorithm (MOGA)

  • Performance Evaluation

  • Conclusion


MOGA

MOGA Overview


MOGA

Chromosomes


MOGA

Fitness Vector

Minimize Allocation p-norm

Minimize VB p-norm

Minimize size


MOGA

Dominating Tree


MOGA

Genetic Operations

  • Crossover: exchange part of genes

  • Mutation: flip the gene values

  • Dominatee Mutation:


MOGA

Algorithm

Return the fittest

Replacement

Selection

Population Initialization

Recombination

Evaluation Process


Outline

  • Motivation

  • Problem Definition

  • Multi-Objective Genetic Algorithm (MOGA)

  • Performance Evaluation

  • Conclusion


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


Outline

  • Motivation

  • Problem Definition

  • Multi-Objective Genetic Algorithm (MOGA)

  • Performance Evaluation

  • 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


Q & A


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