传感器节点用于数据采样和二氧化碳浓度与空气湿度、温度和光照强度的相关性分析

A. Arshad, Novian Habibie, A. Wibisono, P. Mursanto, W. S. Nugroho, W. Jatmiko
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引用次数: 2

摘要

二氧化碳(CO2)气体包含在我们的空气中,它在环境中有许多作用,但在巨大的量变得危险。为了解决这个问题,二氧化碳监测系统是必要的。其中最有效的方法之一就是利用无线传感器网络(WSN)。该系统能够使用传感器节点监测二氧化碳浓度和其他变量。但并非所有这些参数都与二氧化碳浓度有关。为了使监测系统高效运行,需要对各变量之间进行相关性分析。本研究利用自行研制的数字传感器节点采集的数据,对CO2浓度与湿度、温度、光照强度进行相关性分析。在一个环境条件波动的地点连续7天收集数据。用Spearman的rho方法计算相关性。结果表明,CO2与空气湿度与空气湿度呈强正相关(0.726),与光照强度呈弱负相关(- 0.319),与气温无相关(- 0.008)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor node for data sampling and correlation analysis of CO2 concentration with air humidity, temperature, and light intensity
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).
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