Air quality monitoring system for indoor environments using IoT

Biswal, Amita; Subhashini, J.; Pasayat, Ajit Kumar

doi:10.1063/1.5112365

Cited by 16 publications

(5 citation statements)

References 12 publications

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“…Many researchers have worked on the use of ensemble learning techniques in professing solutions to diverse problems. To overcome the gap of inconsistent performance accuracy, [30] used it to develop a model for checking air quality with the use of Bagging and Boosting for pollution forecasting. Compared to SVM, the developed model had enhanced performance accuracy.…”

Section: Application Of Ensemble Learningmentioning

confidence: 99%

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Akpan,

Ajayi,

Adelowo

2023

CTICTR

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An increase in industrialization has caused the release of harmful gases into the atmosphere and this has resulted in environmental pollution that greatly affects the health of both plants and animals alike. Over time, sensor-based technology has been used to develop gas detection systems, but they were faced with some major challenges, such as sensor failure and poor performance that have greatly affected their performance. Also, the use of machine learning techniques has been developed for this purpose by leveraging the powers of sensor-based technology. However, they were faced with issues, such as poor feature selection criteria and missing data, which resulted in delays and inaccurate prediction performance of the models. This study, therefore, seeks to survey existing ensemble learning models and to design a Voting Ensemble Learning Model (VELM) for harmful gas detection systems. The methodology adopted for this study is a comparative analysis of the classification models used for the study. The study concludes that there is a need to develop advanced machine learning models to be used for detecting harmful gas and other detection systems such as earthquake, volcano, and landslide detection systems, in real-time that can inform future research. Also, a recommendation was made for the implementation of the developed model based on its low variance and low bias when dealing with widely dispersed data points in a dataset in addition to its ability to deal with local minima and overfitting that affect classic machine learning. Keywords: Detection System, Ensemble Learning, Machine Learning, Sensor

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Section: Application Of Ensemble Learningmentioning

confidence: 99%

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Akpan,

Ajayi,

Adelowo

2023

CTICTR

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“…Because of its wide detection range, it has rapid reaction, high sensitivity, long life, and stability. [20] DSM501A dust sensor…”

Section: Mq7 Gas Sensormentioning

confidence: 99%

Untitled

2022

AJCSE

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A Smart Environmental Air Pollution Monitoring System (SEAPMS) based on internet of things (IoT) has been built for monitoring the concentrations of major air pollutant gases, as well as a fire extinguishing system for fire detection and prevention. Using a network of sensors, the SEAPMS detects gas concentrations such as carbon monoxide CO, carbon dioxide CO 2 , methane CH 4 , dust, smoke, temperature, and humidity. The sensors will collect data on numerous environmental characteristics and send it to particle photons, which will compare it to a predetermined threshold for each sensor then sends it to a special IoT platform known as UBIDOTS. UBIDOTS sends data to the cloud then they will be realized and virtualized to be displayed. The fire extinguishing system will activate when the smoke concentration exceeds its threshold limit value.

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“…Researchers at the National Physical Laboratory (NPL), New Delhi, have recently developed a low‐cost, low‐energy‐consuming NO 2 sensor based on different forms of graphene that can transmit real‐time monitoring in collaboration with partners from the flagship, the Chalmers University of Technology, The Advanced Institute of Technology, Royal Holloway University and Linköping University to measures the level of NO 2. 10–12 This low‐cost and technologically convenient technology employs a basic chemiresistive technique to measure extremely low NO 2 levels, such as below 10 ppb. The World Health Organization recommends a goal level of NO 2 pollution in cities of 21 ppb, although the normal average level in London is 30–40 ppb 13–15 .…”

Section: Introductionmentioning

confidence: 99%

A review on recent developments and advances in environmental gas sensors to monitor toxic gas pollutants

Saxena

Shukla

2023

Env Prog and Sustain Energy

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Air pollutants originating from various sources like vehicular emission, power stations, factories, refineries, industrial emissions, and burning of garbage in open and laboratories, include many toxic gases and pollutants like hydrogen chloride (HCl), hydrogen sulfide (H2S), and volatile organic compounds (VOCs) like benzene, toluene, xylene, and so on, ammonia (NH3), carbon‐monoxide (CO), carbon‐dioxide (CO2) and nitrogen oxides (NOx), and so on, that are constantly released into the atmosphere and continuously deteriorating our natural environment and surroundings. These pollutants create harmful effects on the ecosystem and affect human health. Though there are many devices available for monitoring these pollutants and toxic gases, they are very expensive and time‐consuming. Since the safety of the life of human health, plants, animals, and their surrounding area on topmost priority, thus there is a significant need to develop user‐friendly and environmentally friendly sensing devices for real‐time monitoring of air pollutant emissions, which are extremely hazardous to the environment, breathable air, and human health. Extensive research has been made by researchers to develop an ideal sensor suitable for the environment and which can be within the reach of the masses. This review article presents different types of gas sensors and the technologies on which they work upon. We have also discussed the different gas sensors with their principle of operation. The working, advantages, and disadvantages of each type of sensor have also been discussed and compared with each other. Despite extensive research over several years to develop a highly sensitive, selective, and quick response sensor for the detection of flammable and toxic gases, the future scope and perspective for scientists are also proposed at the end of this article.

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Air quality monitoring system for indoor environments using IoT

Cited by 16 publications

References 12 publications

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Voting Ensemble Learning Model (VELM) for Harmful Gas Detection Systems: A Proposed Model

Untitled

A review on recent developments and advances in environmental gas sensors to monitor toxic gas pollutants

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