Abstract
This CO2 data is gathered from WSN (Wireless Sensor Network) sensors that is placed in some areas. To make this observation framework run effectively, examining the relationships between factors is required. We can utilize multiple wireless sensor devices. There are three parts of the system, including the sensor device, the sink node device, and the server. We use those devices to acquire data over a three-month period. In terms of the server infrastructure, we utilized an application server, a user interface server, and a database server to store our data. This study built a WSN framework for CO2 observations. We investigate, analyze, and predict the level of CO2, and the results have been collected. The Random Forest algorithm achieved a 0.82 R2 Score.
Highlights
This CO2 data is gathered from Wireless Sensor Networks (WSNs) (Wireless Sensor Network) sensors that is placed in some areas
This CO2 data is gathered from WSN (Wireless Sensor Network) sensors that is placed in some areas
Each node will be connected to 1 other node to be able to send the data from the sensor readings
Summary
Throughout the span of three months, we obtained 6 million instances to be used to analyze and model CO2 behavior. The most frequent value for CO2 data is approximately 200 - 300 ppm. The global CO2 concentration is approximately 450 ppm. The measurement results show that the CO2 concentration is are lower than the global condition. Sometimes the temperature can drop to below 20° when the weather is cold and increase to more than 35° during the day. These data are taken in a tropical environment, which means that there is no winter, summer, autumn, or spring condition
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