Real-time Monitoring Data Research Articles

Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform

The quality of road pavements is highly impacted by environmental variables, such as temperature, humidity, and weather; and construction-related variables, such as material quality and time. In this paper, an advanced data collection and analysis system based on big data/cloud was proposed for the use of IoT location-based smart platforms for pavement quality big data at road pavement sites. For the big data platform, a relational database management system (RDBMS) for a general alphanumeric data-based infrastructure for IoT-based systems was designed based on distributed/parallel processing to enable rapid and big data collection and analysis. The structure was established based on a NoSQL-based database to enable real-time high-speed collection and analysis, and the big data platform was developed as a data collection and visualization infrastructure. When the big data system was studied using data analysis methods, the proposed system demonstrated improvements in data collection performance and analysis speed, indicating that analysis results could be derived in real time. Specifically, the data collection processing (create) speed of the NoSQL-based system (0.405 ms) was significantly higher than that of the compared existing system (21.146 ms). Real-time processing capacity was also verified based on quality big data generated on actual road pavements, and the proposed system was proven suitable for the real-time monitoring (the data collection processing) of road pavement quality big data.

Sustainable Smart Helmet For Coal Miners

The “Sustainable Smart Safety Helmet for Coal Miners” paper aims to enhance the safety and well-being of coal miners by implementing an innovative monitoring system. The paper utilizes the ESP32 microcontroller, integrated with a suite of sensors, including DHT11 for temperature and humidity measurement, MQ135 for gas detection, IR Sensor for helmet status monitoring, and a Heartbeat Sensor for real-time pulse rate tracking. A Buzzer is also integrated to provide immediate audible alerts when potential hazards are detected. The system continuously collects sensor data in real-time, which is then processed and analysed within the ESP32 microcontroller. The data is transmitted to a user-friendly mobile application developed using Blynk. Through the Blynk app, miners and relevant personnel receive critical information regarding environmental conditions and the miners' vital signs. In case of any hazardous conditions, the system generates prompt alerts, triggering the Buzzer and sending notifications to the app to ensure timely responses to potential threats. By combining advanced sensor technology, real-time data monitoring, and swift alert mechanisms, this paper endeavours to safeguard the lives of coal miners in hazardous working environments, providing them with a smart safety solution and empowering them to work with increased confidence and security.

Internet of Things (Iot) and Cyber-Physical Systems for Distributed Sensing and Control

The combination of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) has resulted in a massive increase in the amount of real-time data that is easily available. As a result, this combination has made it possible to obtain previously inaccessible insights into a variety of systems and processes. This has helped us make better judgements, increase productivity, and improve safety and security across a wide range of industries, from manufacturing to medical. Increasing productivity and efficiency is one of the biggest advantages offered by IoT and CPS. Data collection on various systems and processes might reveal inefficiencies and areas for improvement. We can anticipate savings and higher productivity as a consequence. As an extra benefit, real-time data monitoring and analysis enables us to identify any risks to public health and safety and take appropriate action. As a result, accidents can be avoided. However, the widespread adoption of IoT and CPS presents a number of difficulties, including the management of enormous amounts of data and the assurance of privacy and security. Despite these drawbacks, the potential benefits of these technologies are currently too great to ignore. If we continue to support R&D, we may make significant advancements in industries like industrial automation, precision agriculture, and healthcare. It will be vital to overcome these problems if IoT and CPS are to realise their full potential. These challenges include the requirement for better security measures and the creation of increasingly complex data processing algorithms. We can contribute to a safer, wealthier, and more connected society by acting in this manner.

Robotic path planning using NDT ultrasonic data for autonomous inspection

Robot deployed ultrasonic inspection for Non-Destructive Testing (NDT) offers several advantages including time efficiency gains, the reducing of repetitive manual workloads for operators and the enabling of inspection of environments hazardous to human health. Due to accuracy requirements, NDT robotic inspection has traditionally used the concept of digital twins for path planning activities. Recent development has sought to automate this process through visual feedback using low-cost camera sensors. However, these methods do not take into account the use of NDT data itself as part of the robot path planning process. As a consequence, poor path planning accuracy can result due to the inability of conventional cameras to capture internal defects or geometric features. This paper introduces a novel concept of using the NDT ultrasonic data as part of a robotic path planning feedback loop. Firstly, the robot is manually positioned near the start of a weld, and the ultrasonic data is collected. Next, algorithms are implemented to monitor changes in the weld geometry, to determine the robot's movement and pose based on real-time monitoring data, and to enable the robot to autonomously scan a weld with a minimum of operators input, path planning or digital twin. This is advantageous to NDT as visual sensors are unable to monitor geometric features within the weld. The ability to use the NDT data ensures the inspection continues at the optimal configuration (e.g. correct stand off and limiting probe skew), and achieves optimal path planning for NDT robots. The experimental results have shown that the tracking algorithm can effectively and accurately track defects in the sample during the ultrasonic probe detection process with an error rate within ±1 mm.

Cloud computing security in health cyber physical systems

The health industry as one of the Critical Infrastructures has rapidly grown in adopting the use of new ubiquitous technologies in advancing its critical services. The industry because of its high need and usage of new technologies is encountering a myriad of challenges including cyber-attacks. Among the technologies adopted by the health industry is the use of cloud computing services for managing Electronic Medical Records (EMR) on the cloud environment. Today’s medical devices use sensors that collect data from the diverse environments such as the human heart and such data is sent to the cloud for better analysis, monitoring, or management. However, attackers exploit every single development to advance health or medical services like all the other cyber systems. The scope of this paper is to investigate how cloud computing services adopted in Health Cyber Physical Systems can be secured from the prevalent cybersecurity challenges. The study followed a systematic literature review, where data was collected from online databases with set search constraints such as language, date of publication and specific keywords. This paper presents how cloud computing integrated with the medical cyber physical systems can impeccably securely manage the health systems. More specifically the collection, monitoring, and management of real-time data and as a result of this integration, different cyber-attacks are escalating.

System for Monitoring and Identifying theft from Domestic Water Supplies

This system for monitoring and identifying theft from domestic water supplies utilizes advanced sensors and machine learning algorithms to detect and analyze patterns of water usage, identifying anomalies and potential instances of theft. The system can be easily installed and integrated into existing water supply infrastructure, allowing for real-time monitoring and analysis of water usage data. By providing actionable insights and alerts, the system enables water supply companies to quickly respond to instances of theft, reducing losses and ensuring a more equitable distribution of resources. It utilizes various sensors and algorithms to detect anomalies in water usage patterns, which could indicate theft. The system includes a dashboard that allows water supply companies to monitor usage and receive alerts in real-time. By using this system, water supply companies can improve their efficiency, reduce water losses, and save costs associated with theft. The system is easy to install and can be integrated with existing water supply networks. The system has the potential to significantly reduce water losses due to theft, improving water supply sustainability and reducing costs for water providers. The system has the potential to significantly reduce water losses due to theft, improving water supply.

Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks

Recently, the wearable internet of things (WIoT) has brought a new dimension of connectivity to wireless body area networks (WBANs). For instance, the WIoT provides valuable functions, such as collecting, analyzing, and transmitting data for real-time health monitoring of medical services. However, designing a clustering and routing protocol is challenging because WIoT attributes are subject to high interference in dense deployment, social mobility, and limited energy. In a WIoT-enabled WBAN, cooperative control is the fundamental function of clustering and routing. Cluster-based routing protocols are preferred for WBAN because of their scalability. However, one-hop neighbor-based clustering approaches do not ensure connectivity and reachability owing to node mobility. The existing two-hop-based clustering protocols are mostly centralized-based approaches, which are unsuitable for dynamic WBANs. In this paper, we propose a distributed energy-efficient two-hop-based clustering and routing protocol (DECR) targeting WIoT-enabled WBAN. In DECR, in the cluster formation phase, each node obtains the information of its neighbor nodes within the two-hop range. We utilize the modified grey-wolf optimization algorithm for the cluster head (CH) selection and routing optimization. Node connectivity and residual energy were jointly considered when determining the CH in each cluster. We also developed an analytical model to determine the optimal number of clusters by considering intra- and inter-cluster transmission distances to reduce the overall transmission distance and number of transmissions. Finally, we proposed a routing algorithm to ensure energy-efficient packet delivery from CH to sink. Our simulation outcomes revealed that the proposed DECR significantly outperforms the existing clustering and routing protocols in various performance metrics.

Real-time and dynamic estimation of CO<sub>2</sub> emissions from China��s lakes and reservoirs

<p>Lakes and reservoirs act as active carbon (C) reactors and regulators. Both play a crucial terrestrial ecosystem C balance role via carbon dioxide (CO<sub>2</sub>) exchange processes across the water-air interface. It has previously been confirmed that CO<sub>2</sub> flux from lakes and reservoirs generally exhibits significant spatiotemporal heterogeneity. Nevertheless, spatiotemporal CO<sub>2</sub> flux variation has seldom been considered in global and regional CO<sub>2</sub> emission estimates from lakes and reservoirs. By accounting for spatiotemporal CO<sub>2</sub> flux and water surface area variability, we evaluated spatial and temporal CO<sub>2</sub> emission dynamics from China��s inland lakes and reservoirs using national real-time water quality monitoring data and machine learning (ML) models. Between 2021�C2022, we estimated total C emission flux at 6.78 (��2.5) Tg C yr<sup>?1</sup>, where seasonal and regional distribution both exhibited significant heterogeneity. Our state-of-the-art estimate is significantly lower than previous estimates of 7.9~25 Tg C yr<sup>?1</sup> from the 1980s to the 2010s. Water quality parameters (pH and dissolved oxygen [DO]) and climate factors (air temperature) were identified as the general environmental CO<sub>2</sub> flux controls. For the first time, this study clarifies the spatiotemporal patterns and drivers of CO<sub>2</sub> flux from China��s inland lakes and reservoirs, providing a more complete C budget picture of China��s aquatic ecosystems.</p>

The usage of Statistical Process Control (SPC) in Industry 4.0 conditions

Purpose: The purpose of this publication is to present the usage of Statistical Process Control (SPC) approach in Industry 4.0 conditions. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of Statistical Process Control (SPC) with Industry 4.0 signifies a transformative shift in quality management, elevating SPC from a conventional monitoring tool to a proactive force in contemporary manufacturing. Originally employed for ensuring consistent quality through process monitoring, SPC's role has been redefined in the Industry 4.0 era, utilizing data analytics, real-time monitoring, and connectivity to offer a comprehensive understanding of the manufacturing ecosystem. Enabled by the Internet of Things (IoT), SPC gains real-time insights into production processes, crucial for swift anomaly identification and issue resolution. Advanced analytics and artificial intelligence enhance SPC's predictive capabilities, enabling proactive measures to maintain product quality. This publication underscores the significance of SPC in Industry 4.0 conditions, emphasizing its roots in statistical principles, systematic process control, and the distinction between common and special cause variations. The integration of SPC with Industry 4.0 and Quality 4.0 leverages technologies for enhanced quality management, emphasizing real-time data monitoring and collaborative, data-driven approaches. While Table 2 outlines specific aspects of SPC integration, highlighting its versatility, Table 3 enumerates the advantages, emphasizing improved visibility and predictive quality management. However, challenges such as data security concerns and technology integration complexity, outlined in Table 4, necessitate strategic solutions. In conclusion, this integration represents a pivotal advancement, positioning SPC as an indispensable asset for organizations seeking quality excellence, operational efficiency, and resilience in the dynamic landscape of modern manufacturing. Originality/Value: Detailed analysis of all subjects related to the problems connected with the usage of Statistical process Control in Industry 4.0 conditions. Keywords: Industry 4.0; Quality 4.0, quality management; quality methods, SPC, Statistical Process Control. Category of the paper: literature review.

Asphalt Pavement Residual Life Assessment Based on SmartRock Sensors

Real-time structural health monitoring (SHM) is important for the performance evaluation and maintenance management of asphalt pavement. This study applies SmartRock, a wireless embedded sensor widely used in track health monitoring, to the long-term SHM of asphalt pavement to achieve its residual life assessment. According to the structural characteristics of asphalt pavement, a novel processing method of SmartRock monitoring data is proposed. Firstly, An improved genetic algorithm for back-calculation of pavement dynamic modulus using SmartRock monitoring data is proposed. Then, the evaluated dynamic moduli are used to predict the residual rutting life and residual fatigue life of the pavement structure. Finally, the two types of residual life are combined to achieve the pavement residual life. This research uses the real-time monitoring data of a test road in Shandong Province, China, to carry out a case study to verify the proposed approach.

Deformation Analysis of the Cable Reinforcement System of Large Section Box Culvert Roof Entry and Downward Penetration

For the underpass railway large-section box culvert jacking and pipe jacking project, the key to ensuring the normal operation of the existing railway is to maintain the smoothness and stability of the track, railway settlement monitoring and line reinforcement, and box culvert and pipe culvert attitude control are the keys to solving this problem. Taking the Beijing-Harbin high-speed railway project under a city road in Beijing as an example, the law of railway track settlement during the implementation of box culvert jacking was analyzed according to real-time monitoring data. The monitoring results show that accelerating the pouring of the bedding and grouting and strengthening the railway subgrade on both sides of the foundation pit can effectively control the settlement of the railway subgrade and reduce the impact of subsequent construction on the railway, and the line renovation and secondary grouting reinforcement can effectively improve the smoothness of the railway. Combined with the comparative analysis of numerical simulation results, the necessity of line refurbishment and rail fastening combination reinforcement in actual construction is verified.

Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods

Groundwater provides much of the world's potable water. Nevertheless, groundwater quality monitoring programmes often rely on a sporadic, slow, and narrowly focused combination of periodic manual sampling and laboratory analyses, such that some water quality deficiencies go undetected, or are detected too late to prevent adverse consequences. In an effort to address this shortcoming, we conducted enhanced monitoring of untreated groundwater quality over 12 months (February 2019–February 2020) in four shallow wells supplying potable water in Finland. We supplemented periodic manual sampling and laboratory analyses with (i) real-time online monitoring of physicochemical and hydrological parameters, (ii) analysis of stable water isotopes from groundwater and nearby surface waters, and (iii) microbial community analysis of groundwater via amplicon sequencing of the 16S rRNA gene and 16S rRNA. We also developed an early warning system (EWS) for detecting water quality anomalies by automating real-time online monitoring data collection, transfer, and analysis – using electrical conductivity (EC) and turbidity as indirect water quality indicators. Real-time online monitoring measurements were largely in fair agreement with periodic manual measurements, demonstrating their usefulness for monitoring water quality; and the findings of conventional monitoring, stable water isotopes, and microbial community analysis revealed indications of surface water intrusion and faecal contamination at some of the studied sites. With further advances in technology and affordability expected into the future, the supplementary methods used here could be more widely implemented to enhance groundwater quality monitoring – by contributing new insights and/or corroborating the findings of conventional analyses.

Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining

It is the core prerequisite of landslide warning to mine short-term deformation patterns and extract disaster precursors from real-time and multi-source monitoring data. This study used the sliding window method and gray relation analysis to obtain features from multi-source, real-time monitoring data of the Lishanyuan landslide in Hunan Province, China. Then, the k-means algorithm with particle swarm optimization was used for clustering. Finally, the Apriori algorithm is used to mine strong association rules between the high-speed deformation process and rainfall features of this landslide to obtain short-term deformation patterns and precursors of the disaster. The data mining results show that the landslide has a high-speed deformation probability of more than 80% when rainfall occurs within 24 h and the cumulative rainfall is greater than 130.60 mm within 7 days. It is of great significance to extract the short-term deformation pattern of landslides by data mining technology to improve the accuracy and reliability of early warning.

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Using real-time production data of concrete to predict its 28-day compressive strength has great significance for improving the engineering structure quality and overcoming the shortage of the traditional tests long period of concrete compressive strength. The current research has the shortcomings such as insufficient prediction accuracy, inadequate matching between data characteristics and model characteristics, and redundant input parameter information. This paper proposes a BP neural network prediction model optimized by Spearman and PCA. The model first uses Spearman method to reduce the number of the input variables by eliminating variables that have a low correlation with the compressive strength and then uses PCA to eliminate the correlation between material-related variables. Following this, the new uncorrelated input variables optimized by Spearman and PCA are input to the BP neural network-based model to predict the compressive strength of concrete. The results showed that it yielded the mean absolute percentage error (MAPE) of 2.78% and the root mean-squared error (RMSE) of 1.66 MPa, far lower than the error of 4.82% and 2.92 MPa obtained by the nonoptimized BP neural network, respectively. The proposed model fully exploits real-time monitoring data from the concrete mixing station, and its results are close to those of traditional tests. It has great practical significance to guide the concrete production and construction, shorten the production cycle, and reduce the project cost.

Electric Energy Monitoring Node Design Based on NodeMCU and MQTT Protocol

Intelligent electricity management systems can be widely used for monitoring the regional power consumption of enterprises, factories, campuses, properties, etc. To meet the current demand of electricity consumers for monitoring the power data of each node, a node design method based on Modbus and MQTT protocols for monitoring power consumption has been proposed. It can monitor the power, current, voltage, electricity, and other related parameters of the power nodes so that the electricity consumers can control the electric energy data of each power node in real time. The test results show that the node scheme can effectively monitor the data of each power node and realize the real-time monitoring of power consumption data, which shows that the design scheme is effective and feasible.

Deformation-softening behaviors of high-strength and high-toughness steels used for rock bolts

In deep ground engineering, the use of high-strength and high-toughness steels for rock bolt can significantly improve its energy absorption capacity. However, the mechanisms and effects of rock loading conditions on this kind of high energy-absorbing steel for rock bolt remain immature. In this study, taking Muzhailing highway tunnel as the background, physically based crystal plasticity simulations were performed to understand the effect of rock loading rate and pretension on the deformation behaviors of twinning induced plasticity (TWIP) steel used for rock bolt. The material physical connecting to the underlying microscopic mechanisms of dislocation glide and deformation twinning were incorporated in numerical modeling. The rock loading conditions were mimicked by the real-time field monitoring data of the NPR bolt/cable equipment installed on the tunnel surrounding rock surface. The results indicate that the bolt rod exhibits pronounced deformation-softening behavior with decrease of the loading rate. There is also a sound deformation-relaxation phenomenon induced by the dramatic decrease of loading rate after pre-tensioning. The high pretension (>600 MPa or 224 kN) can help bolt rod steel resist deformation-softening behavior, especially at low loading rate (<10−1 MPa/s or 10−2 kN/s). The loading rate was found to be a significant factor affecting deformation-softening behavior while the pretension was found to be the major parameter accounting for the deformation-relaxation scenario. The results provide the theoretical basis and technical support for practical applications.

UAV trajectory planning based on an improved sparrow optimization algorithm with multi-strategy integration

Real-time monitoring of urban high-altitude data is an important goal in the construction and development of smart cities today. However, with the development of modern cities, the monitoring space becomes complicated and narrow because of the different building heights and no-fly zones, which makes UAV trajectory planning more difficult. In this paper, a multi-strategy sparrow search algorithm (MSSA) is proposed to solve the UAV trajectory planning problem in a three-dimensional environment. The algorithm aims to minimize the flight distance and maximize the use efficiency of the UAV. First, the improved algorithm employed a reverse-learning strategy based on the law of refraction to improve the search range and enhance the optimization performance. Second, we introduced a random step size generated by Levy flight into the position update strategy of the participant. The algorithm accuracy and speed of convergence were improved by the randomness feature. Finally, the algorithm incorporated the Cauchy mutation to improve the scout position, which enhanced its ability to jump out of the local optimum of the algorithm. Sixteen benchmark test functions, Wilcoxon rank sum test, and 30 CEC2014 test function optimization results demonstrated that MSSA had better optimization accuracy, convergence speed, and robustness than the comparison algorithms. In addition, the proposed algorithm was applied to the UAV trajectory planning problem in different complex 3D environments. The results confirmed that the MSSA outperformed the other algorithms in complex 3D trajectory planning problems.

Agri-Environment Atmospheric Real-Time Monitoring Technology Based on Drone and Light Scattering

The emission of particulate matter (PM) from agricultural activities, such as concentrated animal feeding, straw combustion, and mechanized harvest, is a hot issue in the sustainable development of agriculture, which has attracted more and more attention from government departments and researchers. However, the research on the transport of particulate matter in the agri-environment still lacks flexible and efficient measurement methods to obtain real-time and accurate spatial distribution data. The objective of our study is to produce a new intelligent platform for agri-environment atmospheric monitoring with high mobility, temporal and spatial resolution, and remote data transmission function to overcome the shortcomings of traditional atmospheric particulate matter monitoring stations, such as small particle size range, immovability, and high cost. Through the light scattering sensor, microcontroller, and wireless data transmission device assembled on the high-mobility drone, the platform could measure the mass concentration of PM2.5, PM10, and TSP at different spatial points in the agri-environment and transmit the measurement data to the receiving device on the ground through three modes: CLOUD, TCP, and UDP. We also developed monitoring software based on the Android platform, which could complete the connection of device and real-time monitoring of measurement data on the ground. Compared with stationary measurement devices, the biggest advantage of our mobile monitoring system is that it has the ability to measure the concentration of TSP and the vertical distribution of PM, which is very important for the research of agricultural environmental particulate matter emission characteristics. After the sensor and communication performance experiments, the sensors had high consistency in the overall change trend, and the communication accuracy rate was high. We carried out a flight measurement comparison experiment at the Wenhua Road Campus of Henan Agricultural University, and the measurement data were highly consistent with the data from the national monitoring stations. We also conducted an agri-environmental atmospheric measurement experiment in Muzhai Village and obtained the vertical distribution data of PM concentration at the nearby measuring point when the harvester was working. The results showed that after the harvester worked for a period of time, the PM2.5, PM10, and TSP concentrations reached the maximum at the altitude of 20 m at the measurement point, which were 80, 198, and 384 μg/m3, respectively, 2.64~3.10 times the particle concentration in the environment before the harvester began to work. Our new platform had high mobility, sensitive reading, and stable communication during the experiment, and had high application value in agricultural environmental monitoring.

Embedded Electronic Sensor for Monitoring of Breathing Activity, Fitting and Filter Clogging in Reusable Industrial Respirators.

Millions of workers are required to wear reusable respirators in several industries worldwide. Reusable respirators include filters that protect workers against harmful dust, smoke, gases, and vapors. These hazards may cause cancer, lung impairment, and diseases. Respiratory protection is prone to failure or misuse, such as wearing respirators with filters out of service life and employees wearing respirators loosely. Currently, there are no commercial systems capable of reliably alerting of misuse of respiratory protective equipment during the workday shifts or provide early information about dangerous clogging levels of filters. This paper proposes a low energy and non-obtrusive functional building block with embedded electronics that enable breathing monitoring inside an industrial reusable respirator. The embedded electronic device collects multidimensional data from an integrated pressure, temperature, and relative humidity sensor inside a reusable industrial respirator in real time and sends it wirelessly to an external platform for further processing. Here, the calculation of instantaneous breathing rate and estimation of the filter's respirator fitting and clogging level is performed. The device was tested with ten healthy subjects in laboratory trials. The subjects were asked to wear industrial reusable respirator with the embedded electronic device attached inside. The signals measured with the system were compared with airflow signals measured with calibrated transducers for validation purposes. The correlation between the estimated breathing rates using pressure, temperature, and relative humidity with the reference signal (airflow) is 0.987, 0.988 and 0.989 respectively, showing that instantaneous breathing rate can be calculated accurately using the information from the embedded device. Moreover, respirator fitting (well-fitted or loose condition) and filter's clogging levels (≤60%, 80% and 100% clogging) also can be estimated using features extracted from absolute pressure measurements combined to statistical analysis ANOVA models. These experimental outputs represent promising results for further development of data-driven prediction models using machine learning techniques to determine filters end-of-service life. Furthermore, the proposed system would collect relevant data for real-time monitoring of workers' breathing conditions and respirator usage, helping to improve occupational safety and health in the workplace.

Response to the COVID-19 Pandemic in Classrooms at the University of the Basque Country through a User-Informed Natural Ventilation Demonstrator.

The COVID-19 pandemic has generated a renewed interest in indoor air quality to limit viral spread. In the case of educational spaces, due to the high concentration of people and the fact that most of the existing buildings do not have any mechanical ventilation system, the different administrations have established natural ventilation protocols to guarantee an air quality that reduces risk of contagion by the SARS-CoV-2 virus after the return to the classrooms. Many of the initial protocols established a ventilation pattern that opted for continuous or intermittent ventilation to varying degrees of intensity. This study, carried out on a university campus in Spain, analyses the performance of natural ventilation activated through the information provided by monitoring and visualisation of real-time data. In order to carry out this analysis, a experiment was set up where a preliminary study of ventilation without providing information to the users was carried out, which was then compared with the result of providing live feedback to the occupants of two classrooms and an administration office in different periods of 2020, 2021 and 2022. In the administration office, a CO2-concentration-based method was applied retrospectively to assess the risk of airborne infection. This experience has served as a basis to establish a route for user-informed improvement of air quality in educational spaces in general through low-cost systems that allow a rational use of natural ventilation while helping maintain an adequate compromise between IAQ, comfort and energy consumption, without having to resort to mechanical ventilation systems.