IoT Enabled Machine Learning for Vehicular Air Pollution Monitoring

Pushpam, V.S. Esther; Kavitha, N.; Karthik, A. Gokul

doi:10.1109/iccci.2019.8822001

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…Another interesting trend is the use of the machine and deep learning algorithms. One such system has produced some promising results in predicting PM values using time series data and applying multilayer neural networks and support vector machine regression techniques [17]. Another system applied three deep learning models viz.…”

Section: Iot-based Solutionsmentioning

confidence: 99%

IoT-Enabled Air Pollution Monitoring Systems: Technologies, Solutions, and Challenges

Kaur

Sharma

2023

Advances in Transdisciplinary Engineering

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Air quality is a crucial aspect of the overall health of any ecosystem. Rapidly increasing urbanization and transportation have proven detrimental to the quality of the air we breathe. Nearly two-thirds of urban air pollution is caused due to vehicular emissions. The harmful pollutants released every day into the atmosphere are deteriorating the environmental and human health. Air pollution has been closely associated with climate change as well as some serious health issues. Hence there is an urgent need for consistent, large-scale air quality monitoring and mitigation strategies. In recent years, the Internet of Things, with its wide range of technologies and some distinct attributes like connectivity, sensing, analyzing and processing capabilities, scalability, and flexibility has provided the world with a dependable option to monitor air pollution in real-time. This paper discusses the key technologies which support IoT-enabled air pollution monitoring systems, proposed solutions, and the challenges faced in the deployment of real-time pollution monitoring systems.

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Section: Iot-based Solutionsmentioning

confidence: 99%

IoT-Enabled Air Pollution Monitoring Systems: Technologies, Solutions, and Challenges

Kaur

Sharma

2023

Advances in Transdisciplinary Engineering

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“…Lora WAN architecture helps in collecting environment particularly roadside data where pollution was created by vehicles emission [10]. Many nodes were created and data from them are observed by gateway and prediction analysis was tested by neural network multilayer perceptron and support vector machine regression algorithm [11].…”

Section: Related Workmentioning

confidence: 99%

Machine learning based Air Pollution Monitoring and predicting cause for pollution

Karthikeyan¹,

Moses²,

Perarasi³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Air pollution has a direct impact on human health through the exposure of pollutants and particulates, which has increased the interest in air pollution and its impacts among the scientific community. The main causes associated with air pollution are the burning of fossil fuels, agriculture, exhaust from factories and industries, residential heating, and natural disasters. The Environmental Protection Agency (EPA) tracks the pollution level by calculating the amount of ground-level ozone (O3), Sulphur dioxide (SO2), particulates matter (PM10 and PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) present in the air molecule. These substances are in compositions of a common index, called the Air Quality Index (AQI), indicating how clean or polluted the air is currently or forecasted to become in areas. This work deliberates the implementation of cloud based IoT system for air quality monitoring in which the sensors are used to calculate CO, PM2.5 and PM10, O3, SO2 and NOx pollution level with environmental condition like temperature and humidity. The obtained information can be updated in cloud platform using Lora nodes and Lora Gateway. The information fetched from the cloud is transmitted to the Machine learning models which contains the detailed dataset for the pollutants and these models accurately predict the day-wise pollutant concentrations and display them using an application. This work presents the detailed analysis for predicting the cause of pollution by using Support Vector Machine (SVM), Random forest algorithm and K-nearest neighbours (KNN) algorithm.

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“…Over time, driver monitoring systems have been developed to monitor and measure drivers' conditions while they are driving [14]. This is due to the progression in autonomous driving technologies, which promote precision driver safety and health [15]. One of the concerns in the driver monitoring system is drowsiness, a condition due to lack of oxygen and increase of air pollutants from the outside environment, such as CO, CO 2 , and NO 2 [16].…”

Section: Related Workmentioning

confidence: 99%

Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique

et al. 2022

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In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.

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IoT Enabled Machine Learning for Vehicular Air Pollution Monitoring

Cited by 14 publications

References 13 publications

IoT-Enabled Air Pollution Monitoring Systems: Technologies, Solutions, and Challenges

IoT-Enabled Air Pollution Monitoring Systems: Technologies, Solutions, and Challenges

Machine learning based Air Pollution Monitoring and predicting cause for pollution

Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique

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