The proliferation and great variety of low-cost air quality (AQ) sensors, combined with their flexibility and energy efficiency, gives an opportunity to integrate them into Wireless Sensor Networks (WSN). However, with these sensors, AQ monitoring poses a significant challenge, as the data collection and analysis process is complex and prone to errors. Although these sensors do not meet the performance requirements for reference regulatory-equivalent monitoring, they can provide informative measurements and more if we can adjust and add further processing to their raw measurements. Therefore, the integration of these sensors aims to facilitate real-time monitoring and achieve a higher spatial and temporal sampling density, particularly in urban areas, where there is a strong interest in providing AQ surveillance services since there is an increase in respiratory/allergic issues among the population. Leveraging a network of low-cost sensors, supported by 5G communications in combination with Artificial Intelligence (AI) techniques (using Convolutional and Deep Neural Networks (CNN and DNN)) to predict 24-h-ahead readings is the goal of this article in order to be able to provide early warnings to the populations of hazards areas. We have evaluated four different neural network architectures: Multi-Linear prediction (with a dense Multi-Linear Neural Network (NN)), Multi-Dense network prediction, Multi-Convolutional network prediction, and Multi-Long Short-Term Memory (LSTM) network prediction. To perform the training of the prediction of the readings, we have prepared a significant dataset that is analyzed and processed for training and testing, achieving an estimation error for most of the predicted parameters of around 7.2% on average, with the best option being the Multi-LSTM network in the forthcoming 24 h. It is worth mentioning that some pollutants achieved lower estimation errors, such as CO2 with 0.1%, PM10 with 2.4% (as well as PM2.5 and PM1.0), and NO2 with 6.7%.

5G communications allow the integration of different wireless technologies to exploit the added value of multihoming scenarios. Furthermore, in connection with the Internet of Things (IoT), multi-homed nodes are very interesting, since it allows one to address new challenges and adapt the usage of communication technologies to new requirements imposed by the location of each deployment, the available energy resources on each node, and the IoT application itself. There are available commercial wireless nodes that embed and handle multiple interfaces. Terms such as seamless and transparent communications as well as technology integration fit well within the 5G standards. However, in practice, these wireless nodes are basically relayed in one of these wireless technologies when connected, and in the case of using other interfaces, their usage is not coordinated. In this context, we propose and implement a novel and efficient heterogeneous interface selection algorithm for multihoming and integrative communications using commercial products by performing local decisions on a per-packet basis, analysing different issues and approaches from a practical point of view, both hardware and software. We compare our proposal with other alternatives, highlighting the opportunities by exploiting these multihoming features in 5G-enabled IoT nodes.

The Internet of Things (IoT) is a network widely used with the purpose of connecting almost everything, everywhere to the Internet. To cope with this goal, low cost nodes are being used; otherwise, it would be very expensive to expand so fast. These networks are set up with small distributed devices (nodes) that have a power supply, processing unit, memory, sensors, and wireless communications. In the market, we can find different alternatives for these devices, such as small board computers (SBCs), e.g., Raspberry Pi (RPi)), with different features. Usually these devices run a coarse version of a Linux operating system. Nevertheless, there are many scenarios that require enhanced computational power that these nodes alone are unable to provide. In this context, we need to introduce a kind of collaboration among the devices to overcome their constraints. We based our solution in a combination of clustering techniques (building a mesh network using their wireless capabilities); at the same time we try to orchestrate the resources in order to improve their processing capabilities in an elastic computing fashion. This paradigm is called fog computing on IoT. We propose in this paper the use of cloud computing technologies, such as Linux containers, based on Docker, and a container orchestration platform (COP) to run on the top of a cluster of these nodes, but adapted to the fog computing paradigm. Notice that these technologies are open source and developed for Linux operating system. As an example, in our results we show an IoT application for soundscape monitoring as a proof of concept that it will allow us to compare different alternatives in its design and implementation; in particular, with regard to the COP selection, between Docker Swarm and Kubernetes. We conclude that using and combining these techniques, we can improve the overall computation capabilities of these IoT nodes within a fog computing paradigm.

Air Quality (AQ) and the management of low-emission zones are critical issues in densely populated urban areas. In such environments, human activity significantly impacts AQ, prompting increased efforts to monitor it using a range of devices. Traditional Air Quality monitoring relies on regulated stations, which are often scarce due to high costs, leaving many areas unmonitored. Low-cost sensors offer a promising solution by enabling the higher-spatial-resolution monitoring of pollution levels. In this article, we present the results of a case study conducted in an urban setting where AQ is affected by human activity, particularly during Las Fallas, Valencia’s most renowned festival, which has been declared an Intangible Cultural Heritage of Humanity by UNESCO. The festival features widespread bonfires, firecrackers and large crowds, all of which contribute to worsening air pollution. In this context, we evaluate the performance of the off-the-shelf, low-cost ZPHS01B multisensor module in a real deployment. This module is capable of monitoring Temperature (T), Relative Humidity (RH), Particulate Matter (PM), CO, ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, ${\mathrm{O}}_3$, ${\mathrm{CH}}_2$O and Volatile Organic Compounds. We analyze the features and properties of these sensors. In our deployments, the ZPHS01B module is connected to an ESP32 microcontroller and assembled into an AQ Internet of Things (IoT) node. We present AQ monitoring results from the festival and compare the measurements with those from regulated AQ monitoring stations, used as a reference. Additionally, we evaluate the power consumption of this AQ IoT node, providing its electrical operating characteristics and considering the use of duty cycles to reduce consumption while maintaining sensor stability. We conclude that this module offers promising capabilities for identifying pollution risk zones and opens the door to new research opportunities, particularly in efficient sensor calibration and AQ parameter prediction.

The increase and diversity of low-cost air quality (AQ) sensors, as well as their flexibility and low power consumption, offers us the opportunity to integrate them into broad AQ wireless sensor networks, with the aim of enabling real-time monitoring and higher spatial sampling density of pollution in all parts of cities. Considering that the vast majority of the population lives in cities and the increase in respiratory/allergic problems in a large part of the population, it is of great interest to offer services and applications to improve their quality of life by avoiding pollution exposure in their movements in the open air. In the ECO4RUPA project, we focus on this kind of service, proposing an inclusive and intelligent routing ecosystem carried out using a network of low-cost AQ sensors with the support of 5G communications along with official AQ monitoring stations, using spatial interpolation techniques to enhance its spatial resolution. The goal of this service is to calculate healthy walking and/or cycling routes according to the particular citizen’s profile and needs. We provide and analyse the results of the proposed route planner under different scenarios (different timetables, congestion road traffic, and routes) and different user profiles, with a special interest in citizens with asthma and pregnant women, since both have special needs. In summary, our approach can lead to an approximately average reduction in pollution exposure of 17.82% while experiencing an approximately average increase in distance travelled of 9.8%.

More than 2 million people have died in the world in 2019 exposed to hazard substances. In this context, it is of paramount importance to deliver effective systems to help minimizing such number of dead. The usage of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), plays a critical role in the detection of hazardous substances within Industry 4.0. The combination of these technologies enhance the efficiency and accuracy of monitoring harmful materials, improving safety standards and operational processes in industrial environments. AI can be used to analyse vast amounts of data for identifying patterns and predicting potential hazards, while IoT connects various devices and sensors to ensure real-time tracking and prompt responses to risks. This technological synergy is essential for modern industries aiming to create safer and more automated systems. In this work, we propose a 5G AI-IoT e-Nose system for the real-time detection and classification of 5 hazardous odours. The proposed AI model is very lightweight and it is affordable for our IoT micro controller unit. The system has been validated in laboratory conditions, but has the advantage and potential impact to be effective in any scenario.