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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics
Fengmei Liang
Taiyuan University of Technology
Computer Science & IT Electrical & Electronics Engineering Engineering

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Study on the Rough-set-based Clustering Algorithm for Sensor Networks Fengmei Liang; Liyuan Zhang; Peng Sun
Bulletin of Electrical Engineering and Informatics Vol 3, No 2: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v3i2.272

Abstract

The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks.
Study on the Rough-set-based Clustering Algorithm for Sensor Networks Fengmei Liang; Liyuan Zhang; Peng Sun
Bulletin of Electrical Engineering and Informatics Vol 3, No 2: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v3i2.272

Abstract

The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks.
Study on the Rough-set-based Clustering Algorithm for Sensor Networks Fengmei Liang; Liyuan Zhang; Peng Sun
Bulletin of Electrical Engineering and Informatics Vol 3, No 2: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (748.239 KB) | DOI: 10.11591/eei.v3i2.272

Abstract

The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks.
Co-Authors Liyuan Zhang Peng Sun