Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks

1. Particle Swarm Optimisers for Cluster formation inWireless Sensor Networks S. M. Guru, S. K. Halgamuge, and S. Fernando Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP) 2005

2. Outline • 1. INTRODUCTION • 2. PARTICLE SWARM OPTIMISATION • 3. OPTIMISATION OF ENERGY USAGE • 4. EXPERIMENT AND SIMULATION • 5. CONCLUSION

3. 1. INTRODUCTION • Makes energy consumption a critical issue in sensor networks • Each cluster have a cluster-head will communicate with all the member nodes of cluster

4. It is always difficult to find an optimal cluster-head placement • Propose four different Particle Swarm Optimisation methods to clustering in wireless sensor network

5. 2. PARTICLE SWARM OPTIMISATION • An evolutionary computing technique based on principle such as bird flocking • They can evaluate its fitness and the fitness of neighboring particles • Can keep track its solution resulted the best performing particle in neighborhood

6. 設定參數 開始 PSO flow chart NO 估計每一個粒子的適應值 更新每一粒子目前的速度與位置 更新目前粒子最佳值與群體最佳值 針對每一個粒子隨機產生初始位置和速度 是否達到最大的搜尋次數 YES 結束 Source:應用粒子群最佳化演算法於多目標存貨分類之研究(93 元智大學碩士論文)

7. Velocity and the position update equations its best position velocity inertia weight acceleration coefficients best position of entire group position

10. Four different PSO methods • A. PSO- Time Varying Inertia Weight (TVIW) • B. PSO-Time Varying Acceleration Coefficients (TVAC) • C. Hierarchical Particle Swarm Optimizer with Time Varying Acceleration Coefficients (HPSO-TVAC) • D. Particle Swarm Optimisation with Supervisor- Student Model (PSO-SSM)

11. 2-A PSO- Time Varying Inertia Weight (TVIW)[9] • Inertia weight varying with time from 0.9 to 0.4 • Acceleration coefficient is set to 2 current iteration number maximum iteration

12. 2-B PSO-Time Varying Acceleration Coefficients (TVAC)[10] • The c1 varies from 2.5 to 0.5 • The c2 varies from 0.5 to 2.5 cognitive component social component

13. 2-C Hierarchical Particle Swarm Optimizer with TVAC (HPSO-TVAC)[10] • When the velocity stagnates in the search space are automatically generated velocity

14. 2-D. Particle Swarm Optimisation with Supervisor-Student Model (PSO-SSM)[11] • Momentum factor (mc) to update the positions • When particle's fitness at the current iteration is not better than previous iteration • The velocity as a navigator (supervisor) - right direction • The position (student) - right step size along the direction

16. 3. OPTIMISATION OF ENERGY USAGE • Energy Model

17. 4. EXPERIMENT AND SIMULATION • 2 models about sensor nodes: • Node can transmit or receive data from all the other nodes • Nodes can transmit and receive data upto a certain distance

18. user-defined

19. Overlapping of clusters which may lead to nodes involved in two or more clusters

20. Communication energy of all the clusters The summation of distances of all no node to their nearest CH Weights were experimentally

21. Square sensor field

22. Simulation Strategies • 100-node networks • Sink location: (50,175) , (50,50) • Clusters: 6 • Maxiter: 1000 • Particles: 30 • v range and x range: 【0：100】

23. The boundary checking

28. 5. CONCLUSION • Different PSO for solving the clustering problem in wireless sensor networks • Boundary checking routine avoid the particle moves outside the set boundary • Many AI algorithms can solve many problem but how to collocate is difficult issue