@article{645e821d13494884987c8479d8a94508,
title = "Dataset of short-term prediction of CO2 concentration based on a wireless sensor network",
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.",
keywords = "CO monitoring system, CO prediction, IoT system, Prediction system, Wireless sensor network",
author = "Ari Wibisono and Wisesa, {Hanif Arief} and Novian Habibie and Aulia Arshad and Aditya Murdha and Wisnu Jatmiko and Ahmad Gamal and Indra Hermawan and Siti Aminah",
note = "Funding Information: Funding: United States Agency for International Development (USAID) through the Sustainable Higher Education Research Alliance (SHERA) Program for Universitas Indonesia's Scientific Modeling, Application, Research and Training for City-centered Innovation and Technology (SMART CITY) Project, Grant #AID-497-A-1600004, Sub Grant#IIE- 00000078-UI-1.] No:0141 /UN2.R3.SC/ HKP. 05.01/2018 Funding Information: This article's publication is partially supported by the United States Agency for International Development (USAID) through the Sustainable Higher Education Research Alliance (SHERA) Program for Universitas Indonesia's Scientific Modeling, Application, Research, and Training for City-centered Innovation and Technology (SMART CITY) Project, Grant #AID-497-A-1600004, Sub Grant#IIE- 00000078-UI-1.] No:0141 /UN2.R3.SC/ HKP. 05.01/2018. We affirm that the submission represents original work that has not been published previously and is not currently being considered by another journal. Additionally, we confirm that each author has seen and approved the contents of the submitted manuscript. This work is supported by the Faculty of Computer Science, Universitas Indonesia Publisher Copyright: {\textcopyright} 2020 The Authors",
year = "2020",
month = aug,
doi = "10.1016/j.dib.2020.105924",
language = "English",
volume = "31",
journal = "Data in Brief",
issn = "2352-3409",
publisher = "Elsevier BV",
}