@JournalArticle {3161,
	title = {Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context},
	journal = {MDPI - IoT 2020},
	volume = {1},
	year = {2020},
	month = {2020-10-23 00:00:00},
	pages = {286-308},
	publisher = {MDPI},
	abstract = {
In a context of increased environmental awareness, the Internet of Things has allowed individuals or entities to build their own connected devices to share data about the environment. These data are often obtained from widely available low-cost sensors. Some companies are also selling low-cost sensing kits for in-house or outdoor use. The work described in this paper evaluated, in the short term, the performance of a set of low-cost sensors for temperature, relative humidity, atmospheric pressure and carbon dioxide, commonly used in these platforms. The research challenge addressed with this work was assessing how trustable the raw data obtained from these sensors are. The experiments made use of 18 climatic sensors from six different models, and they were evaluated in a controlled climatic chamber that reproduced controlled situations for temperature and humidity. Four CO2 sensors from two different models were analysed through exposure to different gas concentrations in an indoor environment. Our results revealed temperature sensors with a very high positive coefficient of determination (r2 >= 0.99), as well as the presence of bias and almost zero random error; the humidity sensors demonstrated a very high positive correlation (r2 >= 0.98), significant bias and small-yet-relevant random error; the atmospheric pressure sensors presented good reproducibility, but further studies are required to evaluate their accuracy and precision. For carbon dioxide, the non-dispersive infra-red sensors demonstrated very satisfactory results (r2 >= 0.97, with a minimum root mean squared error (RMSE) value of 26 ppm); the metal oxide sensors, despite their moderate results (minimum RMSE equal to 40 ppm and r2 of 0.8{\textendash}0.96), presented hysteresis, environmental dependence and even positioning interference. The results suggest that most of the evaluated low-cost sensors can provide a good sense of reality at a very good cost{\textendash}benefit ratio in certain situations
},
	keywords = {data analysis, Environmental monitoring, low-cost sensors, sensor accuracy},
	doi = { https://doi.org/10.3390/iot1020017},
	url = {https://www.mdpi.com/2624-831X/1/2/17},
	author = {Ara{\'u}jo, T. and Silva, L. T. and Moreira, A. J. C.}
}