Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components.

This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement points. Given the fact that those monitoring stations and measurement points are located in different places, it is important to incorporate their time series data into a spatiotemporal dimension. The output can be used as input for various predictive analyses, in particular, we used the reconstructed dataset as input for grid-based (Convolutional Long Short-Term Memory and Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms.

Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid.

Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also making it easier to apply preventive and mitigating measures. Machine learning technologies with available methods and capabilities, combined with the geospatial dimension, can perform predictive analyses with higher accuracy and, as a result, can serve as a supportive tool for productive management.

Data on CO, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic.

In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020-2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors.

Data on records of environmental phenomena using low-cost sensors in vineyard smallholdings.

Inadequate weather conditions are one of the main threats to the correct development of sensitive crops, where a bad situation can lead to greater stress on plants and their weakness against various diseases. This statement is especially decisive in the cultivation of the vineyard. Meteorological monitoring of vineyard parcels is vital to detect and prevent possible fungal diseases.

Reliability Evaluation of the Data Acquisition Potential of a Low-Cost Climatic Network for Applications in Agriculture.

Temperature, humidity and precipitation have a strong influence on the generation of diseases in different crops, especially in vine. In recent years, advances in different disciplines have enabled the deployment of sensor nodes on agricultural plots. These sensors are characterised by a low cost and so the reliability of the data obtained from them can be compromised, as they are built from low-confidence components.

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes.

Nowadays, the concept of "Everything is connected to Everything" has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform.

Interactive guidelines: Public communication of data-based research in cities.

Scientific research results are traditionally published as articles in peer-reviewed conference proceedings or journals. These articles often use technical jargon, which precludes the general public from consuming the results achieved. New ways to communicate scientific results are thus necessary to transfer scientific insights to non-experts, and this work proposes the concept of interactive guidelines to fill this gap.

Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks.

Nowadays, citizens have a huge concern about the quality of life in their cities, especially regarding the level of pollution. Air quality level is of great importance, not only to plan our activities but also to take precautionary measures for our health. All levels of governments are concerned about it and have built their indexes to measure the air quality level in their countries, regions or cities.

SEnviro: a sensorized platform proposal using open hardware and open standards.

The need for constant monitoring of environmental conditions has produced an increase in the development of wireless sensor networks (WSN). The drive towards smart cities has produced the need for smart sensors to be able to monitor what is happening in our cities. This, combined with the decrease in hardware component prices and the increase in the popularity of open hardware, has favored the deployment of sensor networks based on open hardware.