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Intuitionistic Fuzzy Estimations of the Ant Colony Optimization

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Intuitionistic Fuzzy Estimations of the Ant Colony Optimization Powered By Docstoc
					 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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posted:6/4/2012
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