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Meta-optimization for Charger Deployment in Wireless Rechargeable Sensor Networks

Meta-optimization for Charger Deployment in Wireless Rechargeable Sensor Networks. Advanced Computing and Networking Laboratory National Central University Department of Computer Science and Information Engineering Student : Yen-Chung Chen Advisor: Dr. Jehn-Ruey Jiang 2016 / 6.

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Meta-optimization for Charger Deployment in Wireless Rechargeable Sensor Networks

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  1. Meta-optimization for Charger Deployment in Wireless Rechargeable Sensor Networks Advanced Computing and Networking Laboratory National Central University Department of Computer Science and Information Engineering Student : Yen-Chung Chen Advisor: Dr. Jehn-Ruey Jiang 2016/6 Advanced Computing And Networking Laboratory

  2. Outline Introduction Experiment & Modeling Problem Definition & Goals Related Work Proposed Method Simulation Conclusion Advanced Computing And NetworkingLaboratory

  3. Outline Introduction Experiment & Modeling Problem Definition & Goals Related Work Proposed Method Simulation Conclusion Advanced Computing And NetworkingLaboratory

  4. Introduction-WSNs An event occurs !! !! sink Advanced Computing And NetworkingLaboratory

  5. Introduction-Hole An event occurs ?? sink Advanced Computing And NetworkingLaboratory

  6. Introduction-Network Partition An event occurs !! ?? sink Advanced Computing And NetworkingLaboratory

  7. Introduction-WRSN Energy Source Solar energy Heat Radio frequency Energy Harvester Energy-DC Advanced Computing And NetworkingLaboratory

  8. Outline Introduction Experiment & Modeling Problem Definition & Goals Related Work Proposed Method Simulation Conclusion Advanced Computing And NetworkingLaboratory

  9. Experiment-Equipment Energy harvester Wireless charger 3~5m Advanced Computing And NetworkingLaboratory

  10. Experiment Advanced Computing And NetworkingLaboratory

  11. Experiment Advanced Computing And NetworkingLaboratory

  12. Experiment z D y x Advanced Computing And NetworkingLaboratory

  13. Modeling-Experiment Results D D Power (mW) Power (mW) Advanced Computing And NetworkingLaboratory

  14. Modeling-Charging Efficiency Power Regression Analysis Advanced Computing And NetworkingLaboratory

  15. Modeling-Charging Efficiency Advanced Computing And NetworkingLaboratory

  16. Outline Introduction Experiment & Modeling Problem Definition & Goals Related Work Proposed Method Simulation Conclusion Advanced Computing And NetworkingLaboratory

  17. Problem Definition-Scenario H W L Advanced Computing And NetworkingLaboratory

  18. Motivations and Goals • Motivations: • Wireless chargers are expensive. For example, the Powercast TX91501-3W-ID charger currently costs about 1,000 US dollars. • We use particle swarm charger deployment(PSCD) to optimize the number of chargers, but its parameters influencethe PSCD’s performance. • Goals: • Minimize the number of chargers • Optimize parameters of the PSCD Advanced Computing And NetworkingLaboratory

  19. Outline Introduction Experiment & Modeling Problem Definition & Goals Related Work Proposed Method Simulation Conclusion Advanced Computing And NetworkingLaboratory

  20. Related Work-Assumption Advanced Computing And NetworkingLaboratory

  21. Related Work-Assumption Advanced Computing And NetworkingLaboratory

  22. Related Methods–Greedy Cone Covering(GCC) g A C D B Advanced Computing And NetworkingLaboratory

  23. Related Methods–Greedy Cone Covering(GCC) g 1 r A C D B Advanced Computing And NetworkingLaboratory

  24. Related Methods–Greedy Cone Covering(GCC) (iii) (ii) (i) i i j i j j Advanced Computing And NetworkingLaboratory

  25. Related Methods–Adaptive Cone Covering(ACC) g A B E D C Advanced Computing And NetworkingLaboratory

  26. Related Methods–Adaptive Cone Covering(ACC) g A B E D C Advanced Computing And NetworkingLaboratory

  27. Related Methods–Adaptive Cone Covering(ACC) g A B E D C Advanced Computing And Networking Laboratory

  28. Related Methods–Charging Efficiency Greedy Cone Selection (CE-GCS) C3 C2 C1 0.5 B A 0.46 C D 0.07 0.36 0.33 0 0 0 Advanced Computing And NetworkingLaboratory

  29. Related Methods–Charging Efficiency Greedy Cone Selection (CE-GCS) C3 C2 C1 D 0.37 0 Advanced Computing And NetworkingLaboratory

  30. Related Works–Particle Swarm Optimization (PSO) • Proposed by James Kennedy & Russell Eberhart in 1995 • Inspired by social behavior of birds and fishes • Combines self-experience with social experience • Population-based optimization Advanced Computing And NetworkingLaboratory

  31. Particle Swarm Optimization Fitness function Fitness value Particle • Swarm: a set of particles (S) • Particle: • Position: • Velocity: • Each particle maintains • Particle best position (PBest) • Swarm maintains its global best position (GBest) Advanced Computing And NetworkingLaboratory

  32. PSO Algorithm Gbest Pbest V(t) X(t) Particle’s velocity Advanced Computing And NetworkingLaboratory

  33. PSO Algorithm X(t+1) Gbest social V(t+1) Pbest cognitive inertia V(t) X(t) Particle’s velocity Advanced Computing And NetworkingLaboratory

  34. PSO Algorithm • Basic algorithm of PSO • Initialize the swarm form the solution space • Evaluate the fitness of each particle • Update individual and global bests • Update velocity of each particle using(1): • Update position of each particle using(2): • Go to step2, and repeat until termination condition Advanced Computing And NetworkingLaboratory

  35. Related Works–Genetic algorithm Originally developed by John Holland (1975). Inspired by the biological evolution process. Uses concepts of “Natural Selection” (Darwin1859). Advanced Computing And NetworkingLaboratory

  36. Related Works–Genetic algorithm Gene Chromosome Advanced Computing And NetworkingLaboratory

  37. Related Works–Genetic algorithm Gene Binary encoding Gene Chromosome String encoding Chromosome Gene Real-value encoding Chromosome Advanced Computing And NetworkingLaboratory

  38. Related Works–Genetic algorithm Population Advanced Computing And NetworkingLaboratory

  39. Related Works–Genetic algorithm Population offsprings (Chromosomes) parents • Crossover • Mutation Genetic operators Evaluation (fitness) Reproduction (selection) Mates (recombination) Mating pool Advanced Computing And NetworkingLaboratory

  40. Outline Introduction Experiment & Modeling Problem Definition & Goals Related Work Proposed Method Simulation Conclusion Advanced Computing And NetworkingLaboratory

  41. Methods-Genetic Particle Swarm Charger Deployment(GPSCD) • We propose an algorithm Genetic Particle Swarm Charger Deployment(GPSCD) to optimize the number of chargers Advanced Computing And NetworkingLaboratory

  42. Methods-Genetic Particle Swarm Charger Deployment(GPSCD) Population offsprings (Chromosomes) 5. Crossover 6. Mutation parents Genetic operators 2. Evaluation (PSCD) 3. Elitism strategy Mates (recombination) Mating pool 4. Reproduction Advanced Computing And NetworkingLaboratory

  43. Methods-Genetic Particle Swarm Charger Deployment(GPSCD) • ω:inertia weight • c1: cognitive parameter • c2: social parameter • Vmax,:  maximum velocity Advanced Computing And NetworkingLaboratory

  44. Methods-Genetic Meta-Optimization of Particle Swarm Charger Deployment(GMOPSCD) Step1. Random generate the population Population Advanced Computing And NetworkingLaboratory

  45. Methods-Genetic Particle Swarm Charger Deployment(GPSCD) Population offsprings (Chromosomes) 5. Crossover 6. Mutation parents Genetic operators 2. Evaluation (PSCD) 3. Elitism strategy Mates (recombination) Mating pool 4. Reproduction Advanced Computing And NetworkingLaboratory

  46. Fitness function-Particle Swarm Charger Deployment(PSCD) Population Fitness function Fitness value low PSCD The number of chargers Advanced Computing And NetworkingLaboratory

  47. Fitness function-Particle Swarm Charger Deployment(PSCD) • Position: Advanced Computing And NetworkingLaboratory

  48. Fitness function-Particle Swarm Charger Deployment(PSCD) • PSCD Fitness Function: Advanced Computing And NetworkingLaboratory

  49. Fitness function-Particle Swarm Charger Deployment(PSCD) Step 1 : Randomly generate particles’ velocity and positon to initialize H W L Advanced Computing And NetworkingLaboratory

  50. Fitness function-Particle Swarm Charger Deployment(PSCD) • Step 2 : Calculates fitness values for each particle H W L Advanced Computing And NetworkingLaboratory

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