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.

中文翻译：

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环境监测无线传感器网络中低功耗聚类的混沌精英生态位进化算法

近年来，随着人们对环境质量的需求增加，监视诸如温度和氧气含量之类的敏感参数已成为必然。环境监测无线传感器网络（EMWSN）由于其灵活性和高监测精度而成为研究热点。针对EMWSN中的低功率聚类问题，提出了一种混沌精英生态位进化算法（CENEA）。为了验证CENEA的性能，本文进行了仿真实验。通过仿真实验，在相同的条件参数下，将CENEA与改组的蛙跳算法（SFLA），差分进化算法（DE）和遗传算法（GA）进行了比较。结果表明，CENEA平衡了节点能量并提高了节点能量使用效率。与SFLA相比，CENEA的网络能耗降低了8.3％，比DE降低了3.9％，比GA降低了4.6％。此外，CENEA提高了精度并最大程度地减少了计算时间。