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

1. 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

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

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

4. Motivation Load-Balanced Virtual Backbone (LBVB) 1 2 1 2 3 4  3 4 5 6 7 8 5 6 7 8 LBVB MCDS

5. Motivation Dominator Partition 1 2 1 2 3 4 3 4  5 6 7 8 5 6 7 8 Imbalanced Dominator Partition Balanced Dominator Partition

6. Motivation Transitional Region Phenomenon

7. 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

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

9. 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

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

11. MOGA MOGA Overview

12. MOGA Chromosomes

13. MOGA Fitness Vector Minimize Allocation p-norm Minimize VB p-norm Minimize size

14. MOGA Dominating Tree

15. MOGA Genetic Operations • Crossover: exchange part of genes • Mutation: flip the gene values • Dominatee Mutation:

16. MOGA Algorithm Return the fittest Replacement Selection Population Initialization Recombination Evaluation Process

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

18. 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

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

20. 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

21. Q & A