Particle Swarm Optimisers for Cluster formation in

Wireless Sensor Networks

S. M. Guru, S. K. Halgamuge, and S. Fernando

Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP) 2005

Outline

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

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

It is always difficult to find an optimal cluster-head placement

Propose four different Particle Swarm Optimisation methods to clustering in wireless sensor network

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

PSO flow chart

開始

設定參數

針對每一個粒子隨

機產生初始位置和

速度

估計每一個粒子的適應值

更新目前粒子最佳值

與群體最佳值

更新每一粒子目前的速

度與位置

是否達

到最大

的搜尋

次數

結束

NO

YES

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 )

velocity

position

inertia weight acceleration coefficients

its best position

best position of entire group

Velocity and the position update equations

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)

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

maximum iteration

current iteration number

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

cognitivecomponent

social component

2-C Hierarchical Particle Swarm Optimizer with TVAC (HPSO-TVAC)[10]

When the velocity stagnates in the search space are automatically generated velocity

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

3. OPTIMISATION OF ENERGY USAGE

Energy Model

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

user-defined

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

Communication energy of all the clusters

Weights were experimentallyThe summation of distances of all no node to their nearest CH

Square sensor field

Simulation Strategies

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

The boundary checking

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