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Sensor node for data sampling and correlation analysis of CO2 concentration with air humidity, temperature, and light intensity

机译:传感器节点,用于数据采样以及二氧化碳浓度与空气湿度,温度和光强度的相关性分析

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Carbon Dioxide gas (CO2) gas contained in our air which has many roles in environment, but in a huge amount it became dangerous. To encounter that, the system for CO2 monitoring is needed. One of the most effective way is using Wireless Sensor Network (WSN). This system capable to monitor concentration of CO2 and another variable using sensor nodes. But not all of that parameters is correlated to concentration of CO2. To make monitoring system runs efficiently, correlation analysis between variables is needed. This research conduct a correlation analysis between concentration of CO2 and humidity, temperature and light intensity from data collected by our own-made digital sensor node. Data gathered for seven days in one location with a fluctuate environment condition. Correlation calculated with Spearman's rho method. The result is CO2 and air humidity have a strong positive correlation with air humidity (0.726), weak negative correlation with light intensity (−0.319), and no correlation with air temperature (−0.008).
机译:我们的空气中所含的二氧化碳气体(CO2)在环境中起着许多作用,但是在很大程度上它变得很危险。为了解决这个问题,需要用于二氧化碳监测的系统。最有效的方法之一是使用无线传感器网络(WSN)。该系统能够使用传感器节点监控CO2和其他变量的浓度。但是,并非所有这些参数都与CO2浓度相关。为了使监控系统高效运行,需要对变量之间进行相关性分析。这项研究从我们自己制造的数字传感器节点收集的数据中进行了二氧化碳浓度与湿度,温度和光强度之间的相关性分析。在一个波动的环境条件下,在一个位置收集了7天的数据。用Spearman的rho方法计算的相关性。结果是CO2和空气湿度与空气湿度(0.726)有很强的正相关关系,与光强度(-0.319)却有较弱的负相关关系,而与气温没有相关性(-0.008)。

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