English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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