OPTIMIZED CLUSTER HEAD SELECTION IN WSN USING

GA AND HSA

By ,

Akhil M KRl no: 124602

OutlineIntroductionPrimitive clustering approachLEACHGenetic AlgorithmHarmony Search Algorithm.Simulation ResultConclusion

IntroductionWSN (Wireless Sensor Network)

Increasing trend to use WSN in many applications.

Tiny sensors are deployed and left unattended.Continuously report the activity of interest.Sensor nodes are

Exposed to harsh environment. Densely deployed. Unattended in nature. Of less cost.So it is difficult to recharge node batteries.

Energy efficiency is a major design goal of WSN.

ClusteringAn approach which can provide,

Data aggregationA better channel allocation scheme.Energy efficient , high throughput networking.

Nodes form groups (Clusters) in different geographical area.

Elect one node as cluster head(CH) to Collect data from member nodes.Remove redundancy and forward to Base station.

CHs transmit for more time and loose more energy.Periodic re-clustering can be done to select new CHs.This distribute the load uniformly to all sensor nodes.

LEACHLow Energy Adaptive Clustering Hierarchy.Features

It is a distributed clustering algorithm.Hence , node itself decides whether to become

a CH or not without base station interference.Re-clustering is done in each round.A node once selected as CH, will not be

selected again in the same round , to avoid node death.

Number of CHs selected may vary in each round.

Decision Rule

Node generates a random number ‘R’ between 0 and 1.

If (R<Threshold(T)) selected as

CH

Reproduced with permission from IEEE 2012

Threshold function ,T(n)

P : percentage of cluster heads. (eg. 0.05%)r : current round number.G : set of nodes that have not been CHs in the

last 1/P rounds.n : node number.

After 1/p rounds process repeats with all nodes included in G.

Genetic Algorithm

What is GA?GA is a search technique used in finding true

or approximate solutions to optimization problems.

They are a class of evolutionary algorithms It make use of the concept of evolutionary

biology techniques such as inheritance, mutation, selection and crossover.

The evolution usually starts from a population of randomly generated individuals.

In each generation, the fitness of every individual in the population is evaluated.

multiple individuals are selected from the current population (based on their fitness), and modified to form a new population.

The new population is then used in the next iteration of the algorithm.

The algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.

Terminology

Individual - Any approximate solution to the problem.

Population – Collection of individuals Chromosome - Blueprint for an individualTrait - features of an individualGenome - Collection of all chromosomes for an

individual

Chromosome, Genes and Genomes

Reproduced with permission from http://www.cs.wmich.edu/

GA ImplementationThe WSN nodes are represented as bits of a

chromosome.A population consists of several chromosomes.For the initial population, cluster heads are

chosen in random.We define fitness parameters which influence

the fitness function.We define a fitness function to chromosome

such that it minimizes the energy consumption.Fitness of chromosomes are evaluated.

FitnessFitness parameters

Direct Distance to Sink (D)

Cluster Distance (C)Cluster Distance -

Standard Deviation (SD):

Transfer Energy (E)Number of

Transmissions (T)

Fitness function

Fitness functionwi =WeightFi = Fitness parameters

GP- Leach algorithm

Reproduced with permission from IEEE 2012

Harmony search algorithmHarmony search is a music-based metaheuristic

optimization algorithm.It was inspired by the observation that the aim

of music is to search for a perfect state of harmony.

The search process in optimization can be compared to a musicians improvisation process.

Harmony search generates harmonies of inputs which it then evaluates for quality.

iterates this process until it finds the best possible harmoney.

Steps in HSAAlgorithm Parameter initialization

HMS(Harmony Memory Size) : The number of solution vectors in Harmony Memory Matrix

Harmony Memory Considering Rate (HMCR) : The rate in which elements of Harmony Memory is considered.

Pitch Adjusting Rate (PAR)Maximum number of searches.

Initialize the harmony memory (HM)The initial HM consists of a HMS number of

randomly generated solutions for the optimization problem

Each row of the HM is a random solution for the optimization problem.

Objective function is evaluated for each harmony vector.

Improvise a new harmony from the HM.Improvisation of the HM is done by generating

a new harmony vector.

HMCR is defined as the probability of selecting a component from the HM members.

Update the HM.Calculate objective function value for newly

generated Harmony vector If obj(new) > obj(old) , then replace the new

harmony vector against worst case.Iterate the steps until met the required

criterion.

Reproduced with permission from IEEE 2012

Simulation ResultSystem Lifetime

Reproduced with permission from IEEE 2012

No of Cluster Heads

Reproduced with permission from IEEE 2012

Energy dissipation in cluster heads.

Reproduced with permission from IEEE 2012

Conclusion The proposed model overcome the

shortcomings of Leach considering node's remained energy and its location which directly affect the energy consumption of the network and improve the energy efficiency.

Thank You