Exact and approximate algorithms for clustering problem in wireless sensor networks

Clustering is an effective method for improving the network lifetime and the overall scalability of a wireless sensor network. The problem of balancing the load of the cluster heads is called load-balanced clustering problem (LBCP), which is an NP-hard problem. In this study, the authors use paramet...

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Vydané v:IET communications Ročník 14; číslo 4; s. 580 - 587
Hlavní autori: Yarinezhad, Ramin, Hashemi, Seyed Naser
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 03.03.2020
Predmet:
approximation theory
communication complexity
FPT algorithm
k‐additive approximation algorithm
LBCP
load‐balanced clustering problem
network lifetime
NP‐hard problem
optimisation
parameterised complexity
pattern clustering
Research Article
resource allocation
telecommunication network reliability
wireless sensor network
wireless sensor networks
LBCP
approximation theory
load-balanced clustering problem
wireless sensor networks
wireless sensor network
communication complexity
parameterised complexity
optimisation
resource allocation
NP-hard problem
pattern clustering
telecommunication network reliability
k-additive approximation algorithm
FPT algorithm
network lifetime
ISSN:1751-8628, 1751-8636
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Abstract Clustering is an effective method for improving the network lifetime and the overall scalability of a wireless sensor network. The problem of balancing the load of the cluster heads is called load-balanced clustering problem (LBCP), which is an NP-hard problem. In this study, the authors use parameterised complexity to cope with this NP-hard problem. The authors show that LBCP can be solved by a k-additive approximation algorithm with a running time of $ 2^{O({k}/{\log k})}+O(n) $2O(k/log⁡k)+O(n), where k is an upper bound on the maximum load assigned to the cluster heads and n is the input size. Also, LBCP is FPT with respect to the maximum load of the sensor nodes and the number of sensor nodes. The authors propose an fpt-algorithm with respect to these parameters for this problem. In addition, they prove that LBCP is $ W[1]\mbox {-}{\rm hard} $W[1]-hard when the number of the cluster heads is selected as the parameter.
AbstractList Clustering is an effective method for improving the network lifetime and the overall scalability of a wireless sensor network. The problem of balancing the load of the cluster heads is called load‐balanced clustering problem (LBCP), which is an NP‐hard problem. In this study, the authors use parameterised complexity to cope with this NP‐hard problem. The authors show that LBCP can be solved by a k‐additive approximation algorithm with a running time of 2O(k/log⁡k)+O(n), where k is an upper bound on the maximum load assigned to the cluster heads and n is the input size. Also, LBCP is FPT with respect to the maximum load of the sensor nodes and the number of sensor nodes. The authors propose an fpt‐algorithm with respect to these parameters for this problem. In addition, they prove that LBCP is W[1]‐hard when the number of the cluster heads is selected as the parameter.
Clustering is an effective method for improving the network lifetime and the overall scalability of a wireless sensor network. The problem of balancing the load of the cluster heads is called load‐balanced clustering problem (LBCP), which is an NP‐hard problem. In this study, the authors use parameterised complexity to cope with this NP‐hard problem. The authors show that LBCP can be solved by a k ‐additive approximation algorithm with a running time of , where k is an upper bound on the maximum load assigned to the cluster heads and n is the input size. Also, LBCP is FPT with respect to the maximum load of the sensor nodes and the number of sensor nodes. The authors propose an fpt‐algorithm with respect to these parameters for this problem. In addition, they prove that LBCP is when the number of the cluster heads is selected as the parameter.
Clustering is an effective method for improving the network lifetime and the overall scalability of a wireless sensor network. The problem of balancing the load of the cluster heads is called load-balanced clustering problem (LBCP), which is an NP-hard problem. In this study, the authors use parameterised complexity to cope with this NP-hard problem. The authors show that LBCP can be solved by a k-additive approximation algorithm with a running time of $ 2^{O({k}/{\log k})}+O(n) $2O(k/log⁡k)+O(n), where k is an upper bound on the maximum load assigned to the cluster heads and n is the input size. Also, LBCP is FPT with respect to the maximum load of the sensor nodes and the number of sensor nodes. The authors propose an fpt-algorithm with respect to these parameters for this problem. In addition, they prove that LBCP is $ W[1]\mbox {-}{\rm hard} $W[1]-hard when the number of the cluster heads is selected as the parameter.
Author Yarinezhad, Ramin
Hashemi, Seyed Naser
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Issue 4
Keywords LBCP
approximation theory
load-balanced clustering problem
wireless sensor networks
wireless sensor network
communication complexity
parameterised complexity
optimisation
resource allocation
NP-hard problem
pattern clustering
telecommunication network reliability
k-additive approximation algorithm
FPT algorithm
network lifetime
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Snippet Clustering is an effective method for improving the network lifetime and the overall scalability of a wireless sensor network. The problem of balancing the...
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wiley
iet
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Publisher
StartPage 580
SubjectTerms approximation theory
communication complexity
FPT algorithm
k‐additive approximation algorithm
LBCP
load‐balanced clustering problem
network lifetime
NP‐hard problem
optimisation
parameterised complexity
pattern clustering
Research Article
resource allocation
telecommunication network reliability
wireless sensor network
wireless sensor networks
Title Exact and approximate algorithms for clustering problem in wireless sensor networks
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