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

2019 ◽  
Vol 9 (9) ◽  
pp. 1831 ◽  
Author(s):  
Xiaozheng Lai ◽  
Ting Yang ◽  
Zetao Wang ◽  
Peng Chen
Keyword(s):  
Kalman Filter ◽  
Air Quality ◽  
Low Cost ◽  
Moving Average ◽  
High Accuracy ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Raspberry Pi ◽  
Air Quality Monitoring

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 SO2, NO2 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.

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

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3021 ◽  
Author(s):  
Zeba Idrees ◽  
Zhuo Zou ◽  
Lirong Zheng
Keyword(s):  
Air Quality ◽  
Power Consumption ◽  
Real Time ◽  
Low Cost ◽  
Edge Computing ◽  
Quality Monitoring ◽  
Quality Data ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Computing Device

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.

scholarly journals C-AQM: A CROWD-SOURCED AIR QUALITY MONITORING SYSTEM

2018 ◽  
Vol XLII-4 ◽  
pp. 491-497
Author(s):  
M. E. Parés ◽  
F. Vázquez-Gallego
Keyword(s):  
Air Quality ◽  
Communication Systems ◽  
Low Cost ◽  
City Council ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Energy Sensor ◽  
Environment Service ◽  
Quality Maps

<p><strong>Abstract.</strong> European cities are currently facing one of the main evolutions of the last fifty years. “Cities for the citizens” is the new leitmotiv of modern societies, and citizens are demanding, among others, a greener environment including non-polluted air. Improved sensors and improved communication systems open the door to the design of new systems based on citizen science to better monitor the air quality. In this paper, we present a system that relies on the already available Copernicus Environment Service, on Air Quality Monitoring reference stations and on a cluster of new low-cost, low-energy sensor nodes that will improve the resolution of air quality maps. The data collected by this system will be stored in a time series database, and it will be available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main challenges imposed by Air Quality Monitoring systems, our proposal to overcome those challenges, and the results of our preliminary tests.</p>

scholarly journals ON THE ORGANIZATION AND VALIDATION OF A PILOT TEST OF A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 361-366
Author(s):  
D. Garcia ◽  
F. Vázquez-Gallego ◽  
M. E. Parés
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Continuous Monitoring ◽  
Low Cost ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Using Data ◽  
Pilot Tests ◽  
Data Calibration

Abstract. The development of new tools that allow continuous monitoring of air quality is essential for the study of actions, in order to improve the levels of pollutants in the air that are harmful to the health of citizens. Cardiovascular and respiratory diseases have been identified as risk factors for death in patients with COVID-19; at the same time, exposure to air pollution is associated with these diseases. In this article, we present the pilot tests of the Crowdsourced Air Quality Monitoring (C-AQM) system, which allows the generation of reliable air pollution maps, using data provided by low-cost sensor nodes. The results verify that the system is correct after performing a data calibration; an improvement in NO2 pollution has been observed on weekends, as well as a situation of less air pollution by NO2 between the first and second pandemic waves in Spain.

scholarly journals MAPPING AIR QUALITY WITH A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM (C-AQM)

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 685-690
Author(s):  
M. E. Parés ◽  
D. Garcia ◽  
F. Vázquez-Gallego
Keyword(s):  
Air Quality ◽  
Communication Systems ◽  
Low Cost ◽  
City Council ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Strong Impact ◽  
Air Quality Monitoring ◽  
Energy Sensor ◽  
Quality Maps

Abstract. World cities are currently facing one of the major crisis of the last century. Some preliminary studies on COVID-19 pandemia have shown that air pollutants may have a strong impact on virus effects. Improved gas sensors and wireless communication systems open the door to the design of new air monitoring systems based on citizen science to better monitor and communicate the air quality levels. In this paper, we present the Crowdsourced Air Quality Monitoring (C-AQM) system, which relies on Air Quality Monitoring reference stations and a cluster of new low-cost and low-energy sensor nodes, in order to improve the resolution of air quality maps. The data collected by the C-AQM system is stored in a time series database and is available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main bases of the C-AQM system as well as the measurements validation campaign carried out.

scholarly journals Low Cost, Multi-Pollutant Sensing System Using Raspberry Pi for Indoor Air Quality Monitoring

2021 ◽  
Vol 13 (1) ◽  
pp. 370
Author(s):  
He Zhang ◽  
Ravi Srinivasan ◽  
Vikram Ganesan
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Low Cost ◽  
Office Building ◽  
Quality Monitoring ◽  
Raspberry Pi ◽  
Air Quality Monitoring ◽  
Residential Unit ◽  
Pm2.5 And Pm10

Deteriorating levels of indoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. A concurrent multi-parameter monitoring approach accounting for most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional equipment in measuring multiple real-time pollutant concentrations include high cost, limited deployability, and detectability of only select pollutants. The aim of this paper is to present a comprehensive indoor air quality monitoring system using a low-cost Raspberry Pi-based air quality sensor module. The custom-built system measures 10 indoor environmental conditions including pollutants: temperature, relative humidity, Particulate Matter (PM)2.5, PM10, Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs). A residential unit and an educational office building was selected and monitored over a span of seven days. The recorded mean PM2.5, and PM10 concentrations were significantly higher in the residential unit compared to the office building. The mean NO2, SO2, and TVOC concentrations were comparatively similar for both locations. Spearman rank-order analysis displayed a strong correlation between particulate matter and SO2 for both residential unit and the office building while the latter depicted strong temperature and humidity correlation with O3, SO2, PM2.5, and PM10 when compared to the former.

Recent advancements in low-cost portable sensors for urban and indoor air quality monitoring

2021 ◽  
Author(s):  
A. Hernández-Gordillo ◽  
S. Ruiz-Correa ◽  
V. Robledo-Valero ◽  
C. Hernández-Rosales ◽  
S. Arriaga
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Portable Sensors

scholarly journals Low cost air quality monitoring

2020 ◽  
Author(s):  
Chekwube A. Okigbo ◽  
Amar Seeam ◽  
Shivanand P. Guness ◽  
Xavier Bellekens ◽  
Girish Bekaroo ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring
Sign in / Sign up

Export Citation Format

Share Document