1. A M ULTI -O BJECTIVE G ENETIC A LGORITHM FOR C ONSTRUCTING L OAD -B ALANCED V IRTUAL B ACKBONES IN P ROBABILISTIC W IRELESS S ENSOR N ETWORKS 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 GLOBECOM 2013

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

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

4. L OAD -B ALANCED V IRTUAL B ACKBONE (LBVB) 4 12 34 5678 12 34 5678  MCDS LBVB Motivation

5. D OMINATOR P ARTITION 5 12 34 5678 12 34 5678  Motivation Imbalanced Dominator PartitionBalanced Dominator Partition

6. T RANSITIONAL R EGION P HENOMENON Motivation 6 Connected Region Transitional region Disconnected region Link length0 – 2.6m2.6m – 6m> 6m 7 > 97%8 > 95% 6 < 5% Node #82715

7. O UR C ONTRIBUTIONS 7 Motivation Highlight the use of lossy links when constructing Virtual Backbone (VB) for Probabilistic WSNs Propose new optimization problem called LBVBP o LBVB construction problem under PNM Propose a MOGA to solve LBVBP Conduct simulations to validate the proposed algorithm

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

9. LBVB IN P ROBABILISTIC WSN S Objectives: Minimum-sized VB Minimize VB p-norm Minimize Allocation p-norm 9 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 = 5.89 Allocation p-norm = 4.19 Allocation p-norm = 3.53

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

11. MOGA O VERVIEW 11 MOGA

12. C HROMOSOMES 12 MOGA

13. F ITNESS V ECTOR 13 MOGA Minimize size Minimize VB p-norm Minimize Allocation p-norm

14. D OMINATING T REE 14 MOGA

15. G ENETIC O PERATIONS 15 MOGA Crossover: exchange part of genes Mutation: flip the gene values Dominatee Mutation:

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

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

18. 18 S IMULATION R ESULTS Performance 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

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

20. C ONCLUSION 20 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. 21 Q & A