The Prototype Monitoring System for Pollution Sensing and Online Visualization with the Use of a UAV and a WebRTC-Based Platform

Chodorek, Agnieszka; Chodorek, Robert R.; Yastrebov, Alexander

doi:10.3390/s22041578

Cited by 10 publications

(5 citation statements)

References 42 publications

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“…Chodorek et al [135] developed a pollution monitoring system named Kestrel based on a quadcopter and low-cost sensors, which included the Seeed Studio HM3301 laser dust sensor for PM 2.5 detection and several semi-selective MOS sensors (Winsen) for VOCs and some inorganic gases detection. The overall system was designed to measure the concentrations of exhaust gases originating from cars.…”

Section: Urban Air Quality Monitoringmentioning

confidence: 99%

Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review

et al. 2022

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Ambient air quality is a complex issue that depends on multiple interacting factors related to emissions coming from energy production and use, transportation, industrial processes, agriculture, and waste and wastewater treatment sectors. It is also impacted by adverse meteorological conditions, pollutants concentrations, their transport and dispersion in the atmosphere, and topographic constraints. Therefore, air pollutants distribution is not uniform and their monitoring at proper temporal and spatial resolution is necessary. Drone-borne analytical instrumentation can fulfill these requirements. Thanks to the rapid development in the drone manufacturing sector as well as in the field of portable detectors construction, applications of unmanned aerial vehicles (UAVs) for atmospheric pollution monitoring are growing. The purpose of this work is to give an overview of this matter. Therefore, this paper contains basic information on UAVs (i.e., description of different types of drones with their advantages and disadvantages) and analytical instrumentation (i.e., low-cost gas sensors, multi-sensor systems, electronic noses, high-accuracy optical analyzers, optical particle counters, radiation detectors) used for the monitoring of airborne pollution. Different ways of payload integration are addressed and examples of commercially available solutions are given. Examples of applications of drone-borne analytical systems for pollution monitoring coming from natural (i.e., volcanoes, thawing permafrost, wildfires) and anthropological (i.e., urbanization and industrialization; extraction, transport and storage of fossil fuels; exploitation of radioactive materials; waste and wastewater treatment; agriculture) sources are also described. Finally, the current limitations and future perspectives are discussed. Although there is a great potential for drones applications in the field of atmospheric pollution monitoring, several limitations should be addressed in the coming years. Future research should focus on improving performances of available analytical instrumentation and solving problems related to insufficient payload capacity and limited flight time of commonly used drones. We predict that applications of drone-assisted measurements will grow in the following years, especially in the field of odor pollution monitoring.

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Section: Urban Air Quality Monitoringmentioning

confidence: 99%

Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review

et al. 2022

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“…There are also a great number of usual applications for the IoT, such as sensors in hospital rooms that may keep track of a patient's vital signs throughout the day to assist medical professionals with diagnosis and treatment. The widespread distribution of COVID-19 has accelerated the development of this technology [48], [55], [123], [125], [136], [149], [204], [253], [297], [304], [319], [330], [338]- [341].…”

Section: Iot Technology In Healthcarementioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

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“…To mitigate these risks, implementing an air quality monitoring system is essential. Monitoring systems, like the developed sensor solution representing environmental parameters through the air quality index, play a vital role in ensuring an indoor environment suitable for habitation [3]. Machine learning models have been employed to analyze data from various sensors and model occupancy patterns [4,5].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

Benzaama,

Touati,

El Mendili

et al. 2024

Energies

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The population of developed nations spends a significant amount of time indoors, and the implications of poor indoor air quality (IAQ) on human health are substantial. Many premature deaths attributed to exposure to indoor air pollutants result from diseases exacerbated by poor indoor air. CO2, one of these pollutants, is the most prevalent and often serves as an indicator of IAQ. Indoor CO2 concentrations can be significantly higher than outdoor levels due to human respiration and activity. The primary objective of this research was to numerically investigate the indoor relative humidity and CO2 in cob buildings through the CobBauge prototype, particularly during the first months following the building delivery. Both in situ experimental studies and numerical predictions using an artificial neural network were conducted for this purpose. The study presented the use of a piecewise autoregressive exogenous model (PWARX) for indoor relative humidity (RH) and CO2 content in a building constructed with a double walling system consisting of cob and light earth. The model was validated using experimental data collected over a 27-day period, during which indoor RH and CO2 levels were measured alongside external conditions. The results indicate that the PWARX model accurately predicted RH levels and categorized them into distinct states based on moisture content within materials and external conditions. However, while the model accurately predicted indoor CO2 levels, it faced challenges in finely classifying them due to the complex interplay of factors influencing CO2 levels in indoor environments.

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The Prototype Monitoring System for Pollution Sensing and Online Visualization with the Use of a UAV and a WebRTC-Based Platform

Cited by 10 publications

References 42 publications

Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review

Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review

Insights on the internet of things: past, present, and future directions

Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study

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