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

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

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

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

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

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

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

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

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

Experiment Advanced Computing And Networking Laboratory

Experiment Advanced Computing And Networking Laboratory

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

Modeling-Experiment Results 𝜃 ∅   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

Modeling-Charging Efficiency 𝜃 Power Regression Analysis ∅ Advanced Computing And Networking Laboratory

Modeling-Charging Efficiency 𝑁 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝐴𝑛𝑔𝑙𝑒 Advanced Computing And Networking Laboratory

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

Problem Definition-Scenario W H Advanced Computing And Networking Laboratory

Motivations and Goals Motivations: Goals: 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 influence the PSCD’s performance. Goals: Minimize the number of chargers Optimize parameters of the PSCD Advanced Computing And Networking Laboratory

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

Related Work-Assumption 𝑅 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

Related Work-Assumption Advanced Computing And Networking Laboratory

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Related Works–Genetic algorithm 1 Chromosome Advanced Computing And Networking Laboratory

Related Works–Genetic algorithm 1 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

Related Works–Genetic algorithm 1 1 Population 1 1 Advanced Computing And Networking Laboratory

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

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

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

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

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

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

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

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

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 ) 5 𝑁 𝑖 =( 𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 ) 𝐶_𝑥𝑦 𝑖 =( 𝑋 𝑖 , 𝑌 𝑖 ) Advanced Computing And Networking Laboratory

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reproduction- Roulette Wheel’s Selection Reference: http://www.edc.ncl.ac.uk/highlight/rhjanuary2007.php Advanced Computing And Networking Laboratory

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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