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 Networking Laboratory

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

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

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

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

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

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

9. Experiment-Equipment
Wireless charger
Energy
harvester
3~5m
60 °
Advanced Computing And Networking Laboratory

10. Experiment
Advanced Computing And Networking Laboratory

11. Experiment
Advanced Computing And Networking Laboratory

12. Experiment 𝜃 ∅ x y z D
Advanced Computing And Networking Laboratory

13. Modeling-Experiment Results𝜃 ∅
0 °
15 °
30 °
45 °
60 °
75 °
90 °
0.5m
17.63
16.38
13.89
9.89
3.96
2.15
0.37 1m
6.3
5.72
2.29
1.14
0.58
1.5m
1.93
1.26 1
0.72
0.42 2m
1.39
1.04
0.8
0.57
0.25
2.5m
0.84
0.74
0.62
0.34 3m
0.47
0.31
0.18
3.5m
0.28
0.15 4m
0.21
0.11
4.5m
0.14
0 °
15 °
30 °
45 °
60 °
75 °
90 °
0.5m
4.1
3.22
2.16
1.32
0.68
0.22
0.06 1m
1.47
1.25
0.63
0.3
0.19
0.1
  ─
1.5m
0.45
0.36
0.24
0.13
0.09  ─ 2m
0.32
0.23
0.21
0.08
2.5m
0.2
0.14
0.18
0.07 3m
0.11
0.05
3.5m 4m
4.5m
0.04
0 °
15 °
30 °
45 °
60 °
75 °
90 °
0.5m
17.63
13.52
9.07
5.55
2.78
0.92
0.26 1m
6.3
5.23
2.65
1.26
0.79
0.41
1.5m
1.93
1.52
1.02
0.56
0.29 2m
1.39
0.95
0.87
0.34
0.18
2.5m
0.84
0.61
0.76
0.28 3m
0.47
0.42
0.36
0.15
3.5m
0.21
0.19 4m
0.14
4.5m
0.12
0 °
15 °
30 °
45 °
60 °
75 °
90 °
0.5m
4.1
3.81
3.23
2.3
0.92
0.5
0.09 1m
1.47
1.33
0.53
0.27
0.13  ─
1.5m
0.45
0.29
0.23
0.17
0.1 2m
0.32
0.24
0.19
0.07
2.5m
0.2
0.14
0.06
0.03 3m
0.12
3.5m
0.05 4m
0.04
4.5m D D
Power (mW)
Power (mW)
Advanced Computing And Networking Laboratory

14. Modeling-Charging Efficiency𝜃
Power Regression Analysis ∅
Advanced Computing And Networking Laboratory

15. Modeling-Charging Efficiency𝑁
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝐴𝑛𝑔𝑙𝑒
Advanced Computing And Networking Laboratory

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

17. Problem Definition-ScenarioW H
Advanced Computing And Networking Laboratory

18. Motivations and Goals Motivations: Goals:
Wireless chargers are expensive. For example, the Powercast TX W-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 influence the PSCD’s performance.
Goals:
Minimize the number of chargers
Optimize parameters of the PSCD
Advanced Computing And Networking Laboratory

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

20. Related Work-Assumption𝑅 𝜃 𝑁
此篇論文假設充電器的有效充電空間為為一個圓錐(cone)，充電器只對圓錐理的感測節點進行充電不對圓椎外的進行充電
Advanced Computing And Networking Laboratory

21. Related Work-Assumption
Advanced Computing And Networking Laboratory

22. Related Methods–Greedy Cone Covering(GCC)𝑅 A C D B
Advanced Computing And Networking Laboratory

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

24. Related Methods–Greedy Cone Covering(GCC)
(ii) 𝑑 𝑖𝑗 >2𝑟
(iii) 𝑑 𝑖𝑗 <2𝑟 i
𝑑 𝑖𝑗 i j
𝑑 𝑖𝑗 i j
𝑑 𝑖𝑗 j
Advanced Computing And Networking Laboratory

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

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

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

28. Related Methods–Charging Efficiency Greedy Cone Selection (CE-GCS)
𝐶={𝐶1} C1 C2 C3 4 3 1
0.5 B A
0.46 C D A B C D
0.45
0.4
0.33
0.07
0.36
0.33
Advanced Computing And Networking Laboratory

29. Related Methods–Charging Efficiency Greedy Cone Selection (CE-GCS)
𝐶={𝐶1}
𝐶={𝐶1,𝐶3} C1 C2 C3 1 D
0.37 A B C D
0.33
Advanced Computing And Networking Laboratory

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 Networking Laboratory

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

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

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

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 Networking Laboratory

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 Networking Laboratory

36. Related Works–Genetic algorithm1
Chromosome
Advanced Computing And Networking Laboratory

37. Related Works–Genetic algorithm1
Binary encoding
Chromosome
Gene F r B d A
String encoding
Chromosome
Gene
2.6 8 5
1.5 12
Real-value encoding
Chromosome
Advanced Computing And Networking Laboratory

38. Related Works–Genetic algorithm1 1
Population 1 1
Advanced Computing And Networking Laboratory

39. Related Works–Genetic algorithm
Population
offsprings
(Chromosomes)
parents
Crossover
Mutation 1
Genetic
operators
Evaluation 1
(fitness)
Reproduction
(selection)
Mates
(recombination)
Mating pool
Advanced Computing And Networking Laboratory

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

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 Networking Laboratory

42. Population Genetic 2. Evaluation operators Mating pool 1 1
Methods-Genetic Particle Swarm Charger Deployment(GPSCD)
Population
offsprings
(Chromosomes)
5. Crossover
6. Mutation
parents 1
Genetic
operators
2. Evaluation 1
(PSCD)
3. Elitism strategy
Mates
(recombination)
Mating pool
4. Reproduction
Advanced Computing And Networking Laboratory

43. Methods-Genetic Particle Swarm Charger Deployment(GPSCD)
𝑉 𝑚𝑎𝑥 𝑖𝑓 ≥
𝑉 𝑚𝑎𝑥 =
−𝑉 𝑚𝑎𝑥 𝑖𝑓 ≤
−𝑉 𝑚𝑎𝑥 ω c1 c2
Vmax
ω:inertia weight
c1: cognitive parameter
c2: social parameter
Vmax,:  maximum velocity
Advanced Computing And Networking Laboratory

44. Methods-Genetic Meta-Optimization of Particle Swarm Charger Deployment(GMOPSCD)
Step1. Random generate the population ω c1 c2
Vmax
w_1
c1_1
c2_1
Vmax_1
w_2
c1_2
c2_2
Vmax_2
Population
w_n
c1_n
c2_n
Vmax_n
Advanced Computing And Networking Laboratory

45. Population Genetic 2. Evaluation operators Mating pool 1 1
Methods-Genetic Particle Swarm Charger Deployment(GPSCD)
Population
offsprings
(Chromosomes)
5. Crossover
6. Mutation
parents 1
Genetic
operators
2. Evaluation 1
(PSCD)
3. Elitism strategy
Mates
(recombination)
Mating pool
4. Reproduction
Advanced Computing And Networking Laboratory

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

47. Position: 𝐶_𝑥𝑦 𝑖 =( 𝑋 𝑖 , 𝑌 𝑖 ) 𝑁 𝑖 =( 𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 ) 5
Fitness function-Particle Swarm Charger Deployment(PSCD)
Position:
𝐶_𝑥𝑦 𝑖 =( 𝑋 𝑖 , 𝑌 𝑖 ) 𝑁 𝑖 =( 𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 )
𝑁 𝑖 =( 𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 )
𝐶_𝑥𝑦 𝑖 =( 𝑋 𝑖 , 𝑌 𝑖 )
Advanced Computing And Networking Laboratory

48. PSCD Fitness Function:
Fitness function-Particle Swarm Charger Deployment(PSCD)
PSCD Fitness Function:
𝑓 i = 𝑘=1 n 𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒_𝐶𝐸( 𝑑 𝑘 ,∅( 𝑁 i , 𝐶 i 𝑆 𝑘 )
𝐶 𝑖
𝑑 1
𝑆 1
𝑁 𝑖
𝑑 𝑛
𝑑 3
𝑑 2
𝑆 2
𝑆 3
𝑆 𝑛
Advanced Computing And Networking Laboratory

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

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

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

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

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

54. Step 3 : Update Pbest position
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 3 : Update Pbest position
Pbest A
A_v
A_Pbest
A_Gbest H
A_N
Pbest_N
Gbest_N W L
Advanced Computing And Networking Laboratory

55. Step 4 : Update Gbest position
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 4 : Update Gbest position L W H A
Pbest
A_v
A_N
A_Gbest
Pbest_N
A_Pbest
Gbest_N
Gbest
Advanced Computing And Networking Laboratory

56. Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 5 Check the Gbest’s fitness value is the minimize value zero or not
If Gbest’s fitness value is 0, then go to Step6. Update the particles’ velocity by using randomize generation
If Gbest’s fitness value is larger than 0, then go to Step7.Update the particles’ velocity
Advanced Computing And Networking Laboratory

57. Step 7 : Update velocity of particle by
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 7 : Update velocity of particle by
Pbest L W H A
A_v
A_Pbest
Gbest
A_Gbest
A_N
Pbest_N
Gbest_N
Advanced Computing And Networking Laboratory

58. Step 7 : Update velocity of particle by
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 7 : Update velocity of particle by
Pbest L W H A
A_v
A_Pbest
Gbest
A_Gbest
A_New_v
A_N
Pbest_N
Gbest_N
A_New_N
Advanced Computing And Networking Laboratory

59. Step 7 : Update velocity of particle by
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 7 : Update velocity of particle by
𝑉 𝑚𝑎𝑥 𝑖𝑓 ≥
𝑉 𝑚𝑎𝑥 =
−𝑉 𝑚𝑎𝑥 𝑖𝑓 ≤
−𝑉 𝑚𝑎𝑥
Pbest L W H A
A_v
A_Pbest
Gbest
A_Gbest
A_New_v
Vmax
A_N
Pbest_N
Gbest_N
A_New_N
Advanced Computing And Networking Laboratory

60. Step 7 : Update velocity of particle
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 7 : Update velocity of particle
Pbest L W H A
Gbest
A_New_v
A_New_N
Advanced Computing And Networking Laboratory

61. Step 8 : Update position of each particle
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 8 : Update position of each particle L W H A
Pbest
Gbest A’
A_New_v
A’_N
A_New_N
Advanced Computing And Networking Laboratory

62. Step 8 : Update position of each particle
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 8 : Update position of each particle L W H
Pbest
Gbest A’
A’_N
Advanced Computing And Networking Laboratory

63. Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 9 Determine the number of iterations are reached or not
If the number of iterations are not reached , then return to Step2 , and repeat until termination condition
If the number of iterations are reached , then go to Step10. Update Gbest if the new position is better than that of Gbest.
Advanced Computing And Networking Laboratory

64. Step 11 : 1. Charging the sensors at Gbest position
Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 11 : 1. Charging the sensors at Gbest position
2. Marks the sensor if its charging demand is fulfilled L W H
Pbest
Gbest L W H
Pbest
Gbest A’
Advanced Computing And Networking Laboratory

65. Fitness function-Particle Swarm Charger Deployment(PSCD)
Step 11 Check all of sensor nodes are marked or not
If all of sensor nodes are not marked ,then return to Step2.
If all of sensor nodes are marked, then output the number of chargers and their deployment positions
Advanced Computing And Networking Laboratory

66. Population Genetic 2. Evaluation operators Mating pool 1 1
Methods-Genetic Particle Swarm Charger Deployment(GPSCD)
Population
offsprings
(Chromosomes)
5. Crossover
6. Mutation
parents 1
Genetic
operators
2. Evaluation 1
(PSCD)
3. Elitism strategy
Mates
(recombination)
Mating pool
4. Reproduction
Advanced Computing And Networking Laboratory

67. Population Genetic 2. Evaluation operators Mating pool 1 1
Methods-Genetic Particle Swarm Charger Deployment(GPSCD)
Population
offsprings
(Chromosomes)
5. Crossover
6. Mutation
parents 1
Genetic
operators
2. Evaluation 1
(PSCD)
3. Elitism strategy
Mates
(recombination)
Mating pool
4. Reproduction
Advanced Computing And Networking Laboratory

68. Reproduction- Roulette Wheel’s Selection
Population (size n)
Mating pool (size n)
Chromosomes 4
Chromosomes 5
Chromosomes 3
Chromosomes 6
Chromosomes 7
Chromosomes 8
Chromosomes 1
Chromosomes 2
Chromosomes 3
Chromosomes 4
Chromosomes 5
Chromosomes 6
Chromosomes 7
Chromosomes m
Chromosomes n
Advanced Computing And Networking Laboratory

69. Reproduction- Roulette Wheel’s Selection
Reference:
Advanced Computing And Networking Laboratory

70. Population Genetic 2. Evaluation operators Mating pool 1 1
Methods-Genetic Particle Swarm Charger Deployment(GPSCD)
Population
offsprings
(Chromosomes)
5. Crossover
6. Mutation
parents 1
Genetic
operators
2. Evaluation 1
(PSCD)
3. Elitism strategy
Mates
(recombination)
Mating pool
4. Reproduction
Advanced Computing And Networking Laboratory

71. Selection two of Chromosomes
GPSCD - Crossover
Selection two of Chromosomes
using tournament
Mating pool (size n)
Offsprings Population (size n)
Chromosomes 4
Chromosomes 6
Chromosomes 8
Chromosomes 10
Chromosomes 1
Chromosomes 2
Chromosomes 3
Chromosomes 4
Chromosomes 5
Chromosomes 6
Chromosomes 7
New_Chromosomes 1
New_Chromosomes 2
New_Chromosomes 3
New_Chromosomes 4
New_Chromosomes 5
New_Chromosomes 6
Chromosomes i
Chromosomes j
Tournament 1 (size 𝒏 𝟓 )
Tournament 2 (size 𝒏 𝟓 )
Choose the one with the smallest fitness value (best fitness)
the smallest fitness value of Tournament 1
the smallest fitness value of Tournament 2
New_Chromosomes n
Chromosomes u
Chromosomes k
Chromosomes n
Uniform Crossover
Advanced Computing And Networking Laboratory

72. 1 GPSCD - Uniform Crossover Random generate the probability m%
If m% < Crossover rate 1
Mask ω c1 c2
Vmax
w_20
c1_20
c2_20
Vmax_20
w_7
c1_7
c2_7
Vmax_7
Advanced Computing And Networking Laboratory

73. 1 GPSCD - Uniform Crossover Random generate the probability m%
If m% < Crossover rate 1
Mask ω c1 c2
Vmax
w_7
c1_20
c2_20
Vmax_7
w_20
c1_7
c2_7
Vmax_20
Advanced Computing And Networking Laboratory

74. Population Genetic 2. Evaluation operators Mating pool 1 1
Methods-Genetic Particle Swarm Charger Deployment(GPSCD)
Population
offsprings
(Chromosomes)
5. Crossover
6. Mutation
parents 1
Genetic
operators
2. Evaluation 1
(PSCD)
3. Elitism strategy
Mates
(recombination)
Mating pool
4. Reproduction
Advanced Computing And Networking Laboratory

75. GPSCD - Uniform Mutation
Offsprings Population (size n)
New_Chromosomes 1
New_Chromosomes 2
New_Chromosomes 3
New_Chromosomes 4
New_Chromosomes 5
New_Chromosomes 6
mutate
mutate
mutate
mutate
mutate
mutate
mutate
New_Chromosomes n
Advanced Computing And Networking Laboratory

76. 1 GPSCD - Uniform Mutation Random generate the probability m%
If m% < Mutation rate 1
Mask ω c1 c2
Vmax
w_7
c1_20
c2_20
Vmax_7
Advanced Computing And Networking Laboratory

77. 1 GPSCD - Uniform Mutation Random generate the probability m%
If m% < Mutation rate 1
Mask ω c1 c2
Vmax
w_7
c1_20
c2_20
Vmax_7
Advanced Computing And Networking Laboratory

78. GPSCD - Terminating condition
If the number of generations are not reached, then return
to execute GPSCD.
If the number of generations are reached, then output
the best chromosome’s fitness value(the number of
chargers) and deployment positions.
Advanced Computing And Networking Laboratory

79. Outline Introduction Problem Definition & Goals Related Work Method 1
Expert and Simulation
Method2 to improve Method1
Simulation
Conclusion
Advanced Computing And Networking Laboratory

80. Simulation Environment Item Parameter Region 20 x 15 𝑚 2
Number of sensors
50, 100, 150, 200, 250
Average charging demands of sensors
0.18mW，0.54mW，0.9mW
The Height of Deployment Plane
2.3 𝑚
Advanced Computing And Networking Laboratory

81. Effective Charging Distance
Simulation
Two greedy algorithm (Gcc and Acc)
Item
Parameter
Effective Charging Distance
3.0 𝑚
Angle Threshold
30 °
Grid Length
1.8 𝑚
Advanced Computing And Networking Laboratory

82. The Height of Deployment Plane
Simulation
GPSCD
Item
Parameter
Population 20
Crossover rate
0.55
Mutation rate
0.2
Number of generation
Number of particles
𝟑𝟎𝟎
The Height of Deployment Plane
2.3 𝒎 W
0.1~4 C1 C2
Vmax
20~2000cm
Iteration
300
rand()
0~1
Advanced Computing And Networking Laboratory

83. Simulation - Resault
Low energy consumption scenario (0.18mW:80%, 0.54mW:10%, 0.9mW:10%)
Advanced Computing And Networking Laboratory

84. Simulation - Resault
Medium energy consumption scenario (0.18mW:10%, 0.54mW:80%, 0.9mW:10%)
Advanced Computing And Networking Laboratory

85. Simulation - Resault
High energy consumption scenario (0.18 mW:10%, 0.54 mW:10%, 0.9 mW:80%)
Advanced Computing And Networking Laboratory

86. Outline Introduction Problem Definition & Goals Related Work Method 1
Expert and Simulation
Method2 to improve Method1
Simulation
Conclusion
Advanced Computing And Networking Laboratory

87. Conclusion
We propose a suite of algorithm GPSCD to make sensors sustainable within optimized number of chargers.
We utilize PSO individual memory of the local optimum and PSO group memory of global optimum to adjust locations and antenna orientations of chargers.
We use GA to encode the parameters of the PSCD and find the chromosome with the lowest fitness value to reach the goal of using the minimum number of chargers to fulfill the charging demands of all sensor nodes.
Although the GPSCD execution time up to two days, but it only uses less than half the number of chargers used in the ACC and the GCC .
Advanced Computing And Networking Laboratory

88. Thank you!
Advanced Computing And Networking Laboratory