Wireless sensor network based pollution monitoring system in metropolitan cities

Raipure, Shwetal; Mehetre, Deepak

doi:10.1109/iccsp.2015.7322841

Cited by 24 publications

(14 citation statements)

References 6 publications

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“…There are a wide range of studies covering the issue of application of edge computing on IoT [11][12][13][14][15][16][17][18][19], as well as monitoring [20][21][22][23][24][25][26][27][28] and prediction [29-37] for AQ.…”

Section: Related Workmentioning

confidence: 99%

“…In the study of Rybarczyk and Zalakeviciute [35], based on the J48 decision tree algorithm, two different decision models are constructed to predict the concentration of PM2.5 in the two adjacent regions. In the study of Raipure and Mehetre [21], the ID3 algorithm in the decision tree is applied to calculate the percentage of air pollutants. The algorithm is used to predict specific areas and provide early warning information for highly polluted areas.…”

Section: Prediction Modelmentioning

confidence: 99%

“…In order to achieve higher prediction accuracy, many ML models not only require a large amount of historical data for training (storage problem), but also have a high time complexity throughout the training process (calculation problem) [45]. For example, the decision tree model [21,35] can improve the prediction accuracy via the increase in the number of training samples and the depth of the tree, however, the time complexity O(N × M × D) is also multiplied (N is the number of samples, M is the vector dimension, and D is the tree depth). The ANN [23,36,37] also has the similar problem.…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

“…In their four-tier architecture, customer service interface handles requests from clients via the HTTP protocol, when the management layer receives the data and stores it in the database. Raipure and Mehetre [21] proposed a large city pollution monitoring system based on wireless sensor network. The system uses AVR (Atmel AVR) ATmega-32 microcontroller to transfer the values from ADC (Analog to Digital Converters) to the server, and uses a Bluetooth microcontroller to build a communication channel between gas delivery to the server.…”

Section: Introductionmentioning

confidence: 99%

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IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

et al. 2019

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In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO 2 , NO 2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture. of industry digitalization in agile connectivity, real-time business, data optimization, application intelligence, security and privacy protection, etc. Edge computing features are like human nerve endings, which can self-process simple stimuli and feedback the processed features to the cloud brain.Smart agriculture makes the application of IoT technology in traditional agricultural production more "intelligent" by using sensors and software to control agricultural production through mobile platforms or computer platforms. In smart agriculture, establishing a real-time monitoring and prediction system for air quality (AQ) is the most basic and most important solution [5]. The prediction for AQ is based on the analysis of the monitoring data. In other words, the accuracy of the monitoring data affects the accuracy of the prediction to a certain extent [6]. At the same time, the Environment Agency also has specified specific values for AQ [7]. Once current AQ exceeds the threshold, people should take appropriate countermeasures. However, when the prediction is inaccurate, it will lead to decision errors. In order to obtain high-precision monitoring data, many AQ monitoring schemes currently existing use high-precision sensors. However, high-precision sensors are often accompanied by higher costs. A complete system consists of multiple sets of sensors [8], so there is a trade-off between cost and accuracy. In addition, in a traditional IoT-based AQ monitoring system, the data collected by the sensing layer needs to be uploaded, analyzed and processed in the cloud computing platform at the network layer [9]. However, in China, most agricultural areas are in remote locations and harsh environments limited by bandwidth and network connectivity [10]. The timely ...

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Section: Related Workmentioning

confidence: 99%

Section: Prediction Modelmentioning

confidence: 99%

Section: Kalman Filter Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

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“…1 and 2 These revisions show how difficult it is to model the relationship between standard measurement methods and personal exposure, as well as the relationship between exposure and health. Failure to recognize and comprehend these connections will weaken and obfuscate the global health threats that the world faces [1][2][3][4][5][6][7][8][9]. This section will primarily cover the history and basics of air pollution monitoring in order to keep the atmosphere as safe as possible.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Air Pollution Monitoring System for Smart Cities Using IoT and Machine Learning

2021

IJATCSE

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Today, almost everything is going under automation. Air pollution has become one of the major crises across the globe. According to the report of the World Health Organization (WHO), around 580,000 people died due to air pollution. This document deals with the effective monitoring of air pollution systems. The proposed technique uses machine learning algorithms for the intelligent monitoring of air pollution. The concept of the Internet of Things (IoT) is implemented in the system to make it more reliable and accessible from anywhere throughout the world. ESP32 is used as a microprocessor for decision making purposes. The system uses Arduino software to build an algorithm. The DHT11 module is used to sense the humidity as well as temperature. MQ-2, MQ-7 and MQ-135 are used for sensing the level of methane, carbon monoxide and for measuring air quality, respectively. A buzzer is used to identify any unusual condition. Our work considers pollution caused by vehicles and provides an in-the-moment solution that does not directly monitor pollution levels, as well as control measures for reducing traffic in extremely polluted areas. This system will undoubtedly be on humans' behalf in such a way that a smart city will have much less time for spending, and there will undoubtedly be other industries, and the air will undoubtedly be extra polluted, and this device will undoubtedlyallow people to understand how safe the air is.

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RETRACTED CHAPTER: Internet of Things Based Air Pollution Monitoring and Forecasting System

Gokul

Srikanth

Inbarasu

et al. 2019

Sustainable Communication Networks and Application

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Wireless sensor network based pollution monitoring system in metropolitan cities

Cited by 24 publications

References 6 publications

IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

Intelligent Air Pollution Monitoring System for Smart Cities Using IoT and Machine Learning

RETRACTED CHAPTER: Internet of Things Based Air Pollution Monitoring and Forecasting System

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