Improving Energy Consumption Using Fuzzy-GA Clustering and ACO Routing in WSN

Majeed, Doaa Mohsin; Rabee, Hasan Wahhab; Ma, Zhenjie

doi:10.1109/icaibd49809.2020.9137446

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…Majeed et al [30] combine energy and node location to introduce a cost function and uses GA to perform cluster 2 Journal of Sensors head election. So that nodes with higher energy and better locations are elected as cluster heads, making the tasks of each node more reasonable.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Elite Niche Evolutionary Algorithm for Low‐Power Clustering in Environment Monitoring Wireless Sensor Networks

Liu

Yang

et al. 2021

Journal of Sensors

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In recent years, as people’s demand for environmental quality has increased, it has become inevitable to monitor sensitive parameters such as temperature and oxygen content. Environmental monitoring wireless sensor networks (EMWSNs) have become a research hotspot because of their flexibility and high monitoring accuracy. This paper proposes a chaotic elite niche evolutionary algorithm (CENEA) for low-power clustering in EMWSNs. To verify the performance of CENEA, simulation experiments are carried out in this paper. Through simulation experiments, CENEA was compared with shuffled frog leaping algorithm (SFLA), differential evolution algorithm (DE), and genetic algorithm (GA) in the same conditional parameters. The results show that CENEA balances node energy and improved node energy usage efficiency. CENEA’s network energy consumption is reduced by 8.3% compared to SFLA, 3.9% lower than DE, and 4.6% lower than GA. Moreover, CENEA improves the precision and minimizes the computation time.

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Section: Introductionmentioning

confidence: 99%