Wireless sensors network

1. A Detail Study and Implementation of
Sensor Node Deployment in Wireless
Sensor Networks Based on Particle
Swarm Optimization
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2. Keywords
• Introduction
• Components of WSN
• Wireless Sensor Network
• Communication in Wireless Sensor Network
• Network Architecture
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3. Introduction
• A wireless sensor network is a collection of
nodes organized into a cooperative network.
• Each node has capability to sense the data,
process the data. These sensors work with
each other to sense some physical
phenomenon.
• The nodes in the network are connected via
Wireless communication channels.
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4. Components of WSN
• sensor
– A transducer
– converts physical phenomenon e.g. heat, light, motion, vibration
,into the electrical signals.
• sensor node
– basic unit in sensor network
– contains on-board sensors, processor, memory, transceiver, and
power supply
• sensor network
– consists of a large number of sensor nodes
– nodes deployed either inside or very close to the sensed
phenomenon
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5. Wireless Sensor Network
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6. Communication in Wireless Sensor Network
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7. Network Architecture
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8. Network Architecture
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9. Node Architecture
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11. Problem Statement
The main idea of this project is to solve the
coverage problem in wireless sensors network by
increasing sensors node coverage percentages, due
to that, effect of number of sensors nodes ,size of
region of interest (ROI) and Particle swarm
optimization algorithm are studied. pso is an
optimization methods that can be deployed to
achieve higher coverage percentage.
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12. PSO AND IMPROVING
• Particle swarm optimization is one of the
newest evolutionary algorithms inspired from
swarm intelligence.
• PSO is an outstanding algorithm for solving
multidimension function optimization and has a
series of advantages such as high speed
regional convergence and efficient global
searching.
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13. Definition
Particle swarm optimization is a population
based stochastic optimization technique
developed by Dr. Eberhart and Dr. Kennedy in
1995, inspired by social behavior of birds
flocking or fish schooling.
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14. Literature survey
• j.Kennedy and r.elvert,”partical swarm
optimization“ in proc .IEEE int.conf
neural network ,vol.4, nov-1,1995.
• “Study on coverage in wireless sensors
network using grid based strategy and
particle swarm optimization” W.Z.wan
ismail and S.Abd.Manaf.
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15. • “Sensors node deployment in wireless sensors
network based on particle swarm optimization”
Zhiming li lin lei, IEEE int. conference -2009.
• “Random ,PSO & MDBPSO based sensors
deployment in Wireless sensors
networks”,Aparna pradeep laturkar,Vol.10,No.
1,April 2018.
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16. Methodology & Algorithm
PSO simulates the behaviors of birds flocking.
Suppose the following scenario:
• A group of birds are randomly searching food in an
area.
• There is only one piece of food in area being
searched.
• All the birds do not know where the food is. But they
know how far the food is in each iteration.
So what’s the best strategy to find the food?
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17. • The effective one is to follow the bird which is
nearest to the food.
• In PSO, each single solution is a “bird” in the search
space. We call it “particle”.
• All of particles have fitness value which are evaluated
by the fitness function to be optimized,
and have velocities which direct the flying of the
particles.
• The particles fly through the problem spaces by
following the current optimum particles.
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18. • PSO is initialized with a group of random particles (solutions)
and then searches for optima by updating generations.
• In every iteration , each particle is updated by following two
“best” values.
-- The first one is the best solution (fitness) it has achieved
so far.(the fitness value is also stored.)
This value is called pbest.
-- another “best” value that is tracked by the particles swarm
optimizer is the best value, obtained so far by any particle in the
population. This best value is global best and called gbest.
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19. • After finding the two best values, the particle
updates its velocity and positions with following
equation (a) and (b).
v[]=w*v[]+c1*rand()*(pbest[]-present[])+c2*rand()*(gbest[]- present[])
Present[]=present[]+v[]
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20. Where,
W= inertia weight(monitor the effect of previous
velocity in current search).
V[] is the particle velocity.
present[] is the current particle.
Pbest[] and gbest[] are defined as stated before.
rand() is a random number between (0,1).
c1,c2 are learning factors. Usuallyc1=c2=2.

21. Flow chart
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23. Queries
?
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