Cluster Head Selection for Equal Cluster Size in Wireless Sensor Networks

Nam, Choon-Sung; Ku, Yong-Ki; Yoon, Jong-Wan; Shin, Dong-Ryeol

doi:10.1109/niss.2009.182

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…LEACH cannot guarantee equal clustering. This can cause imbalance of a local cluster [6]. Figure 1 shows this well.…”

Section: Releated Workmentioning

confidence: 82%

“…On the contrary to this, if cluster is not distributed equally, that can cause energy imbalance. That means that the energy imbalance between clusters can cause excessive energy drain of cluster heads and reduce network lifetime [6]. So, this paper proposes energy-efficient cluster head selection algorithm based on energy consumption of nodes considering unequal clustering.…”

Section: Introductionmentioning

confidence: 99%

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Setting up the threshold based on cluster head selection algorithm in wireless sensor networks

Nam

Cho

Shin

2010

2010 2nd International Conference on Education Technology and Computer

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For network life time, load balancing and scalability, wireless sensor networks have to configure the network based on local management. Clustering algorithm is for satisfying above requirements. A cluster head selection of this algorithm requires to solve additional communication cost and unequal clustering compared with previous clustering algorithm. So, this paper proposes novel cluster head selection algorithm revised previous clustering algorithm.Keywords-wireless sensor networks, clustering, cluster head selection algorithm I.978-1-4244-6370-11$26.00

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“…LEACH cannot guarantee equal clustering. This can cause imbalance of a local cluster [6]. Figure 1 shows this well.…”

Section: Releated Workmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Setting up the threshold based on cluster head selection algorithm in wireless sensor networks

Nam

Cho

Shin

2010

2010 2nd International Conference on Education Technology and Computer

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“…In 2009, Choon-Sung Nam et al [13], depicted a CH selection model for uniform cluster sizes in WSNs. This method also presents multi-hop cluster formation technique with a constraint communication nodes range to adjust anticipated model.…”

Section: Related Workmentioning

confidence: 99%

Effectual Clustering and Node Placement with Differential Evolution Particle Swarm Optimization using Markov Chain Clustering in FANET

2019

IJEAT

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Flying ad hoc network (FANET) comprises of multiple unmanned aerial vehicles (UAVs) which is effectual technology for future generation. In this investigation, the specific way for constructing a FANET topology using clustering technique to achieve end-to-end communication is elaborated. For this purpose, an application that uses the meta-heuristics approach for cluster analysis is anticipated. Specifically, the parameters of differential evolution (DE) and particle swarm optimization (PSO) have gained the attention and extensive popularity in various communities based on its working effectiveness in resolving complex combinational optimization crisis. Thus, hybrid model of DE and PSO based Markov Chain Clustering Model (MCCM) is designed in this investigation to analyse the problems of clustering in FANET and reliability parameters are examined. The proposed (DEPSO-MCM) model is to enhance search capability and to attain superior flexibility in forming nodes cluster. Empirical outcomes demonstrate DEPSOMCM based clustering algorithm attains superior performance in number of epochs to acquire fitness function effectually. The simulation was carried out in NS-2 simulator, the outcomes based on the simulation shows that the proposed method works effectually and shows better trade-off than the existing techniques to provide a meaningful clustering.

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“…The second variant that we examine in this paper, the sizeconstrained CH selection, is more related to the work done by Nam et al [16] where the CH selection tries to form clusters of equal size. Chatterjee et al [17] also propose a distributed clustering algorithm that takes into consideration various parameters such as ideal degree, transmission power, etc., while limiting the number of members in each cluster.…”

Section: Related Workmentioning

confidence: 99%

New findings on the complexity of cluster head selection algorithms

Nguyen

Minet

Kunz

et al. 2011

2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

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Clustering schemes offer a practical way of providing scalability when dealing with large and dense Mobile Ad hoc Networks (MANETs). The feasibility of clustering can be determined by the complexity of the cluster head selection. Optimizing the cluster head selection allows for the network to be more efficient by minimizing the signaling overhead while ensuring that the network connectivity is maintained despite topology changes. In this paper, we present some new findings on the complexity of the cluster head selection algorithms. Two variants of the cluster head selection are examined: (1) the distance-constrained selection where every node in the network must be located within a certain distance to the nearest cluster head; and (2) the size-constrained selection where each cluster is only allowed to have a limited number of members. We show that the problem of minimizing the set of cluster heads is NPhard for both variants. We propose two distributed selection algorithms, each having logarithmic approximation ratio, for these variants. We also discuss, using NS-2 simulations, the resulting cluster size distribution and cluster head density, which impact the efficient operation of the network.

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Cluster Head Selection for Equal Cluster Size in Wireless Sensor Networks

Cited by 14 publications

References 9 publications

Setting up the threshold based on cluster head selection algorithm in wireless sensor networks

Setting up the threshold based on cluster head selection algorithm in wireless sensor networks

Effectual Clustering and Node Placement with Differential Evolution Particle Swarm Optimization using Markov Chain Clustering in FANET

New findings on the complexity of cluster head selection algorithms

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