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S&M1966 Research Paper of Special Issue https://doi.org/10.18494/SAM.2019.2403 Published: September 9, 2019 Improvement of Energy Efficiency in Wireless Sensor Networks Using Low-energy Adaptive Clustering Hierarchy (LEACH)-based Energy Betweenness Model [PDF] Yu-Fan Feng, Shu-Guang Pan, Zhi-Yong Huang, and Hsiung-Cheng Lin (Received April 9, 2019; Accepted June 26, 2019) Keywords: sensor network, energy betweenness, protocol of clustering, energy consumption, lifespan of network, balance
The imbalance of energy consumption in wireless sensor networks (WSNs) may affect both network lifetime and reliability. Traditionally, the low-energy adaptive clustering hierarchy (LEACH) protocol has been applied to lower the energy consumption. Although the LEACH protocol can choose cluster heads (CHs) randomly to prevent a number of nodes from premature failure due to overutilization, the discrepancy in the energy distribution under different network topologies may result in a low network performance efficiency. In this paper, the LEACH-energy betweenness (LEACH-EB) model is proposed by taking energy consumption as a constraint condition. It can judge the equilibrium of clustering based on the energy betweenness of each node and realize the optimization of clustering in WSNs. The simulation results verify that the proposed LEACH-EB model can make the clustering more energy-efficient for better performance in terms of reliability and stability than the LEACH protocol. Additionally, the model can significantly reduce the extra energy loss caused by uneven clustering and thus prevent the degradation of network performance from the premature senescence of some nodes.
Corresponding author: Hsiung-Cheng LinThis work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yu-Fan Feng, Shu-Guang Pan, Zhi-Yong Huang, and Hsiung-Cheng Lin, Improvement of Energy Efficiency in Wireless Sensor Networks Using Low-energy Adaptive Clustering Hierarchy (LEACH)-based Energy Betweenness Model, Sens. Mater., Vol. 31, No. 9, 2019, p. 2691-2702. |