# Intuitionistic Fuzzy Estimations of the Ant Colony Optimization by yurtgc548

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```									 Wireless Sensor Network
Layout

Stefka Fidanova1, Pencho Marinov1 Enrique Alba2
1Institute of Information and Communication

Technologies – BAS
2University of Malaga, Spain
Contents
•   Telecommunications
•   Wireless Sensor Network
•   Problem Formulation
•   Ant Colony Optimization
•   Computational Results
•   Conclusion and Future Work
Telecommunications
•   Telephones
•   Television
•   Data transmissions
•   Internet
•   Security
Wireless Sensor Network
•   Reconnaissance
•   Surveillance
•   Forest fire prevention
•   Volcano eruption study
•   Health data monitoring
•   Civil engineering
WSN Layout Problem

•   High Energy Communication Node
•   Sensing Radius
•   Communication Radius
•   Fully Covered and Connected Area
•   Minimal Number of Sensors
•   Minimal energy
Objective Function

f1    sensor number
f2    energy
f  f1  f 2
Real Ants Behavior
Ant Colony Optimization
Procedure ACO
Begin
initialize the pheromone
while stopping criterion not satisfied do
position each ant on a starting node
repeat
for each ant do
chose next node
end for
until every ant has build a solution
update the pheromone
end while
end
Transition Probability
  if allowe

ob        
ijij
        j      (t)
Pr 
k                    k
( 
)
t
ij     k)ib
b   t
( ib
allowed


0             otherwi
t ijij1 b
ij ) s (  )
(    l   ij

 there
sensor
the
1 is onposition
b
ij
 position
0the empty
is

 communicat
1if  ion  s 
exists newpoin
covere
l
ij          ij
 ifcommunicat
0 not ion
Pheromone Updating

τ ij  ρτ ij + ( 1 ρ) / f(V)
Computational Example
• Sensing area 500x500
• Coverage radius 30
• Communication radius 30
Computational Results
Algorithm   Min        Min
sensors    energy
Sym         (288,72)   (288,72)

MOEA        (260,123) (291,36)

NSGA        (262,83)   (277,41)

IBEA        (265,83)   (275,41)

ACO         (233,58.8) (239,58)
WSN Layout
Conclusion and Future Work
Thank for Your Attention

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