Raghavendra Khot

Bio: Raghavendra Khot is an academic researcher from Vidyalankar Institute of Technology. The author has contributed to research in topics: Air quality index & Global environmental analysis. The author has an hindex of 1, co-authored 1 publications receiving 14 citations.

Survey on air pollution monitoring systems

Abstract: The global environment is currently facing a major issue of air pollution It is one of the foremost cause of environmental and social health risks in India Air pollution poses a serious threat to living things, eco-system and climate, especially on human health in densely populated urban areas where the pollution levels continuously starts increasing above the safer limits Real time air quality monitoring systems requires special features like exact measurement of the parameters and analysis of the same It makes decision making on timely basis and very easy for monitoring and controlling air quality Currently monitoring urban air quality is critical subject that needs to be looked after for enhancing the well-being of citizens The ultimate target of these systems is to provide exact, live information concerning the air pollution threats and to the relevant authorities for taking the necessary decisions and actions to improve the air quality Real time representation of current scenario can be seen though such applications which allows to do health impact evaluations

Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Abstract: With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT) In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption Our model acquires a power consumption reduction up to 23% with a significant low cost Experimental evaluations were performed under different scenarios to validate the system’s effectiveness

IoT Based Air Pollution Monitoring and Control System

Abstract: Pollution related deaths increase every year and the leading factor for these deaths is air pollution. Air pollution is caused due to various elements among which pollution due to automobiles plays a pivotal role. Our work considers pollution due to automobiles and provides a real time solution which not just monitors pollution levels but also take into consideration control measures for reducing traffic in highly polluted areas. The solution is provided by a sensor based hardware module which can be placed along roads. These modules can be placed on lamp posts and they transfer information about air quality wirelessly to remote server. This information can be used for traffic control. The proposed system also provides information about air quality through a mobile application which enables commuters to take up routes where air quality is good.

An IoT enabled system for enhanced air quality monitoring and prediction on the edge

Abstract: Air pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide. Particulate Matter less than 2.5 µm in diameter (PM2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM2.5 concentration on both edge devices and the cloud. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). It uses the past 24 h of PM2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM2.5. This system was tested on a PC to evaluate cloud prediction and a Raspberry Pi to evaluate edge devices’ prediction. Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. The performance of our system was assessed using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), coefficient of determination (R2), Index of Agreement (IA), and duration in seconds. The obtained results highlighted that NARX/LSTM achieved the highest R2 and IA and the least RMSE and NRMSE, outperforming other previously proposed deep learning hybrid algorithms. In contrast, NARX/XGBRF achieved the best balance between accuracy and speed on the Raspberry Pi.

Towards an integrated framework for air quality monitoring and exposure estimation-a review.

Abstract: For the health and safety of the public, it is essential to measure spatiotemporal distribution of air pollution in a region and thus monitor air quality in a fine-grain manner. While most of the sensing-based commercial applications available until today have been using fixed environmental sensors, the use of personal devices such as smartphones, smartwatches, and other wearable devices has not been explored in depth. These kinds of devices have an advantage of being with the user continuously, thus providing an ability to generate accurate and well-distributed spatiotemporal air pollution data. In this paper, we review the studies (especially in the last decade) done by various researchers using different kinds of environmental sensors highlighting related techniques and issues. We also present important studies of measuring impact and emission of air pollution on human beings and also discuss models using which air pollution inhalation can be associated to humans by quantifying personal exposure with the use of human activity detection. The overarching aim of this review is to provide novel and key ideas that have the potential to drive pervasive and individual centric and yet accurate pollution monitoring techniques which can scale up to the future needs.