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 0.8–0.96), 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–benefit ratio in certain situations.
Highlights
During the last years, collaborative sensing, a concept well described by the authors in [1,2,3], has been used in several application fields due to paradigm-breaking features such as decentralization, the possibility of enhancing the space-time granularity of a sensing system, reduced costs, and its capability of giving users the power to be a node and become part of a solution to a shared concern or common problem
As the cost is a constraint to densifying environmental monitoring networks, the Internet of Things and Collaborative Sensing triggered the creation of complementary techniques to assist environmental monitoring in a creative manner
The sensors were selected using criteria that an individual would have when engaging in an environmental monitoring project on the Internet of Things
Summary
Collaborative sensing, a concept well described by the authors in [1,2,3], has been used in several application fields due to paradigm-breaking features such as decentralization, the possibility of enhancing the space-time granularity of a sensing system, reduced costs, and its capability of giving users the power to be a node and become part of a solution to a shared concern or common problem. On the outside of smart buildings, green walls (or living walls) are a common asset used by modern architecture in order to mitigate heat absorption and reflection in buildings, helping to meet energy efficiency requirements and even soothe the heat island effects in the vicinity [23] In this context, the authors in [24] proposed a solution for the automation of a green wall irrigation system using Internet of Things technologies in conjunction with low-cost environmental sensors and satisfactorily achieved a higher data density for the involved parameters, both in space and time, at the same price of a single system with implied superior quality. The performance indicators were obtained by statistically analysing the sensor readings against reference readings, for temperature, humidity and carbon dioxide, and between sensor pairs, for the atmospheric pressure sensors
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