Estimate Air Quality Based on Mobile Crowd Sensing and Big Data

Feng, Cheng; Wang, Wendong; Tian, Ye; Que, Xirong; Gong, Xiangyang

doi:10.1109/wowmom.2017.7974308

Cited by 13 publications

(13 citation statements)

References 22 publications

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“…Because the number of categories in our classified samples was unbalanced, the true positive rate (TPR) and false positive rate (FPR) were critical performance indicators. Therefore, the receiver operator characteristic (ROC) [38] based on these two indicators was adopted as shown in Figure 7.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Xie

Zhang

et al. 2019

Sensors

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Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have been performed using RSSs, there has been little research on the automatic recognition of on-road high-emitting vehicles. In general, high-emitting vehicles and low-emitting vehicles are classified by fixed emission concentration cut-points, that lack a strict scientific basis, and the actual cut-points are sensitive to environmental factors, such as wind speed and direction, outdoor temperature, relative humidity, atmospheric pressure, and so on. Besides this issue, single instantaneous monitoring results from RSSs are easily affected by systematic and random errors, leading to unreliable results. This paper proposes a method to solve the above problems. The automatic and fast-recognition method for on-road high-emitting vehicles (AFR-OHV) is the first application of machine learning, combined with big data analysis for remote sensing monitoring of on-road high-emitting vehicles. The method constructs adaptively updates a clustering database using real-time collections of emission datasets from an RSS. Then, new vehicles, that pass through the RSS, are recognized rapidly by the nearest neighbor classifier, which is guided by a real-time updated clustering database. Experimental results, based on real data, including the Davies-Bouldin Index (DBI) and Dunn Validity Index (DVI), show that AFR-OHV provides faster convergence speed and better performance. Furthermore, it is not easily disturbed by outliers. Our classifier obtains high scores for Precision (PRE), Recall (REC), the Receiver Operator Characteristic (ROC), and the Area Under the Curve (AUC). The rates of different classifications of excessive emissions and self-adaptive cut-points are calculated automatically in order to provide references for law enforcement departments to establish evaluation criterion for on-road high-emitting vehicles, detected by the RSS.

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Section: Experiments and Discussionmentioning

confidence: 99%

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Xie

Zhang

et al. 2019

Sensors

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“…where β is scattering coefficient and d(i, j) is the scene depth at pixel coordinates (i, j) [24]. For the deflection of light by aerosols in the atmosphere, Fattal [31] presented an image formation model, which can be expressed as follows:…”

Section: Haze Extractionmentioning

confidence: 99%

“…The results obtained using their method were consistent with those obtained through subjective evaluation. Feng et al [24] proposed a PM 2.5 estimation method based on the random forest model. This method requires meteorological data, traffic data, records from monitoring sites, information regarding points of interest, and photographs.…”

Section: Introductionmentioning

confidence: 99%

“…Nonmodel-based techniques do not have any model basis. They mainly extract characteristic values of images, such as the spatial contrast [21], image entropy [22], root mean square of an image [22], and HSI of the sky [24], according to the image blurring phenomenon. Model-based techniques are based on an atmospheric scattering model [24][25][26]; thus, a 2D extinction coefficient profile can be obtained.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Air Quality Monitoring and Alert System Based on Two Image Analysis Techniques for Reportable Fire Events

Liang

et al. 2021

Atmosphere

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In this paper, a new monitoring alert system for air pollution emergencies is proposed. The proposed system can perform air quality monitoring to provide real-time alerts of an individual event. The system uses two image analysis techniques, namely pixel recognition and haze extraction, for video fire smoke detection. The image analysis process is divided into daytime and nighttime image analyses, which involve the analysis of red-green-blue (RGB) and gray scale images. The images analyzed in this study were captured by the video camera of an air quality monitoring station. Seven fire accidents around a selected industrial park and downtown area were analyzed in detail. Among these accidents, three occurred at daytime, one occurred over 7 days, and three occurred at nighttime. Alert models based on pixel recognition and haze extraction were established. These models incorporated the formulas of haze equivalent (HT(t)) and separated pixels (XT(t)), as well as the threshold equations of haze equivalent (∇H) and separated pixels (∇X). An alert signal is sent to the administrator when HT(t) > ∇H or XT(t) > ∇X. The obtained results indicate that a real-time observation and alert system based on two image analysis techniques can be designed for air quality monitoring without expensive hardware devices. This alert system can be used by administrators to understand the course of a reportable event, especially as evidence for the appraisal of fire accidents. It is recommended that this system be connected to the fire brigades in order to obtain early fire information.

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“…Air quality estimation is a typical application in mobile sensing. Feng et al 6 designed a fine-grained PM 2.5 monitoring approach with abundant images, which are collected with mobile sensing. Besides air quality prediction, electromagnetic environment monitoring is also well studied.…”

Section: Crowdsensingmentioning

confidence: 99%

Sparse sensing data–based participant selection for people finding

Tian

Tang

2019

International Journal of Distributed Sensor Networks

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With the emerging of participatory sensing, crowdsensing-based lost people finding is arising. As a special locationcentric task, participant selection is a key factor to determine success or failure of lost people finding result. Besides traditional influence, like Quality of Information contribution, candidate's spatial proximity to the lost people is crucial in participant selection procedure. In order to evaluate how possible a candidate can approach lost people, the probability distribution of their tracing points should be predicted. However, the sparse sensing data problem has been a bottleneck of estimating people's probable position. To overcome these issues, we study the problem of selecting optimal participants according to each candidate's spatial proximity and Quality of Information contribution. In the first procedure, we proposed a Received Signal Strength Indicator Positioning-Determined Compressive Sensing algorithm to interpolate missing Received Signal Strength Indicator data. Then, a location-important marking method is put forward to select a set of high-quality data for estimating missing people's location. In the third procedure, a double-condition greedy participant selection approach, which guarantees candidates' spatial proximity and Quality of Information contribution, is executed to select optimal participants. Simulation results demonstrate that the proposed mechanism outperforms the other algorithms both in accuracy of positioning and quality of uploaded sensing data.

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Estimate Air Quality Based on Mobile Crowd Sensing and Big Data

Cited by 13 publications

References 22 publications

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Integrated Air Quality Monitoring and Alert System Based on Two Image Analysis Techniques for Reportable Fire Events

Sparse sensing data–based participant selection for people finding

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