Improving Low-Energy Adaptive Clustering Hierarchy Architectures with Sleep Mode for Wireless Sensor Networks

Authors:
Young-Long Chen;Neng-Chung Wang;Yi-Nung Shih;Jia-Sheng Lin
Affiliations:
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan 404;Department of Computer Science and Information Engineering, National United University, Miaoli, Taiwan 360;Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan 404;Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan 404
Venue:
Wireless Personal Communications: An International Journal
Year:
2014

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Abstract

A wireless sensor network (WSN) is composed of sensor nodes whose energy is battery-powered. Therefore, the energy is limited. This paper aims to improve the energy efficiency of sensor nodes in order to extend the lifetime of WSNs. In this paper, we propose four new hierarchical clustering topology architectures: random cluster head and sub-cluster head (RCHSCH), random cluster head and max energy sub-cluster head (RCHMESCH), random cluster head and sub-cluster head with sleep mode (RCHSCHSM) and random cluster head and max energy sub-cluster head with sleep mode (RCHMESCHSM). Our proposed architectures involve three-layers and are based on low-energy adaptive clustering hierarchy (LEACH) architecture. Notably, RCHSCH can improve upon cluster head death within the LEACH architecture. In addition, we develop a sleep mode for sensor nodes based on correlations among sensor data within sub-clusters in RCHSCHSM. Thus, we can reduce the energy consumption of the sensor node and increase energy efficiency. From the simulation results, our proposed RCHSCH, RCHMESCH, RCHSCHSM and RCHMESCHSM architectures perform better than the LEACH architecture in terms of initial node death, the number of nodes alive and total residual energy. Furthermore, we find the performance of RCHMESCHSM architecture to be optimal in the set of all available architectures.