A MULTI-OBJECTIVE GENETIC ALGORITHM FORCONSTRUCTING LOAD-BALANCED VIRTUAL

BACKBONES INPROBABILISTIC WIRELESS SENSOR NETWORKS

Jing (Selena) HeDepartment of Computer Science, Kennesaw State University

Shouling Ji and Raheem BeyahSchool of Electrical and Computer Engineering, Georgia Institute of Technology

Yingshu LiDepartment of Compute Science, Georgia State University

GLOBECOM 2013

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OUTLINE

Motivation Problem Definition Multi-Objective Genetic Algorithm (MOGA) Performance Evaluation Conclusion

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OUTLINE

Motivation Problem Definition Multi-Objective Genetic Algorithm (MOGA) Performance Evaluation Conclusion

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LOAD-BALANCED VIRTUAL BACKBONE (LBVB)

1 2

3 4

5 6 7 8

1 2

3 4

5 6 7 8

MCDS LBVB

Motivation

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DOMINATOR PARTITION

1 2

3 4

5 6 7 8

1 2

3 4

5 6 7 8

Motivation

Imbalanced Dominator Partition Balanced Dominator Partition

TRANSITIONAL REGION PHENOMENON

Motivation

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Connected Region

Transitional region

Disconnected region

Link length 0 – 2.6m 2.6m – 6m > 6m

7 > 97% 8 > 95%6 < 5%

Node # 8 27 15

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OUR CONTRIBUTIONS

Motivation

Highlight the use of lossy links when constructing Virtual Backbone (VB) for Probabilistic WSNs

Propose new optimization problem called LBVBPo LBVB construction problem under PNM

Propose a MOGA to solve LBVBP

Conduct simulations to validate the proposed algorithm

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OUTLINE

Motivation Problem Definition Multi-Objective Genetic Algorithm (MOGA) Performance Evaluation Conclusion

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LBVB IN PROBABILISTIC WSNS Objectives:

Minimum-sized VBMinimize VB p-normMinimize Allocation p-norm

Problem Definition

Potential Traffic Load

Actual Traffic Load

• 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 = 8.29 VB p-norm = 5.89Allocation p-norm = 4.19 Allocation p-norm = 3.53

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OUTLINE

Motivation Problem Definition Multi-Objective Genetic Algorithm (MOGA) Performance Evaluation Conclusion

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MOGA OVERVIEWMOGA

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CHROMOSOMESMOGA

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FITNESS VECTORMOGA

Minimize size

Minimize VB p-norm

Minimize Allocation p-norm

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DOMINATING TREEMOGA

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GENETIC OPERATIONS

MOGA

Crossover: exchange part of genes Mutation: flip the gene values Dominatee Mutation:

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ALGORITHM

MOGA

Population Initialization

Evaluation Process

Selection

Recombination

Replacement

Return the fittest

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OUTLINE

Motivation Problem Definition Multi-Objective Genetic Algorithm (MOGA) Performance Evaluation Conclusion

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SIMULATION RESULTSPerformance Evaluation

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

Our method

Others’ Methods

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OUTLINE

Motivation Problem Definition Multi-Objective Genetic Algorithm (MOGA) Performance Evaluation Conclusion

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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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Q & A