Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador

Alvear-Puertas, Vanessa; Burbano-Prado, Yadira A.; Rosero-Montalvo, Paúl D.; Tözün, Pinar; Marcillo, Fabricio; Hernández, Wilmar

doi:10.3390/s22187015

Cited by 26 publications

(15 citation statements)

References 38 publications

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“…The architecture presented by Harbouche et al [ 169 ] is a wireless body sensor network architecture based on a mobile data collector. The architecture presented by Alvear-Puertas et al [ 204 ] is similar. In fact, both are oriented to explain the operation and deployment of the system.…”

Section: Architectures For Iotssmentioning

confidence: 94%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, especially in the Scrum methodology, to determine its use and role in IoTS development. Initially, 4303 documents were retrieved, a number that was reduced to 186 after applying automatic filters and by the relevance of their titles. After analysing their contents, only 60 documents were considered. Of these, 38 documents present the development of an IoTS using some methodology, 8 present methodologies focused on the construction of IoTS software, and 14 present methodologies close to the systems life cycle (SLC). Finally, only one methodology can be considered SLC-compliant. Out of 38 papers presenting the development of some IoTS following a methodology for traditional information systems (ISs), 42.1% have used Scrum as the only methodology, while 10.5% have used Scrum combined with other methodologies, such as eXtreme Programming (XP), Kanban and Rapid Prototyping. In the analysis presented herein, the existing methodologies for developing IoTSs have been grouped according to the different approaches on which they are based, such as agile, modelling, and service oriented. This study also analyses whether the different proposals consider the standard stages of the development process or not: planning and requirements gathering, solution analysis, solution design, solution coding and unit testing (construction), integration and testing (implementation), and operation and maintenance. In addition, we include a review of the automated frameworks, platforms, and tools used in the methodologies analysed to improve the development of IoTSs and the design of their underlying architectures. To conclude, the main contribution of this work is a review for IoTS researchers and developers regarding existing methodologies, frameworks, platforms, tools, and guidelines for the development of IoTSs, with a deep analysis framed within international standards dictated for this purpose.

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Section: Architectures For Iotssmentioning

confidence: 94%

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Guerrero-Ulloa

Rodríguez-Domínguez

Hornos

2023

Sensors

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“…To implement the monitoring campaign, an Rn probe developed in the RnMonitor project [30] was used for in situ investigation, which was in line with approaches presented in previous works [48][49][50][51][52][53][54]. This probe works in continuous mode as a data collection unit.…”

Section: Indoor Rn Concentration Assessmentmentioning

confidence: 99%

Understanding Seasonal Indoor Radon Variability from Data Collected with a LoRa-Enabled IoT Edge Device

2023

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The long-term assessment of radon (Rn) is a critical factor in evaluating the exposure risk faced by building occupants, and it plays a significant role in determining the implementation of Rn remediation strategies aimed at enhancing indoor air quality (IAQ). Meteorological parameters, such as temperature, relative humidity, and atmospheric pressure, as well as geological factors, such as soil properties, uranium content, rock formations, parent rock weathering, and water content, can significantly impact the assessment of Rn exposure risk and the selection of appropriate mitigation measures. A continuous monitoring campaign of a National Architectural Heritage building serving as a museum open to the public for a period of 546 consecutive days was conducted. The results of the in situ investigation revealed a broad range of seasonality in indoor Rn emission, with a negative correlation observed between Rn concentration and air temperature. The data indicated that indoor Rn concentration increases in the winter months as a result of reduced indoor air temperature and decreased air exchange, while it decreases in the summer months due to increased air temperature and enhanced natural ventilation. However, the implementation of high ventilation rates to improve IAQ may result in significant heat losses, thereby affecting the thermal comfort of building occupants during the winter months. Therefore, it is imperative to achieve a balance between ventilation practices and energy efficiency requirements to ensure both IAQ and thermal comfort for building occupants.

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“…Air pollution is currently the most significant environmental risk to human health, and European citizens perceive it as the second-biggest environmental concern after climate change [ 1 ]. Monitoring air quality is of primary importance to encourage more sustainable lifestyles and plan corrective actions [ 2 ]. The essential tool for monitoring air quality is a network of devices [ 3 , 4 ], usually organized in a wireless sensor network supported by Internet of Things (IoT) technology [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection and Repairing for Improving Air Quality Monitoring

Rollo

Bachechi

2023

Sensors

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Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants’ concentrations can support the identification of urban areas with poor air quality and raise citizens’ awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms’ performances.

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Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador

Cited by 26 publications

References 38 publications

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Understanding Seasonal Indoor Radon Variability from Data Collected with a LoRa-Enabled IoT Edge Device

Anomaly Detection and Repairing for Improving Air Quality Monitoring

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