Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks

Ramos, Francisco; Trilles, Sergio; Muñoz, Andrés; Huerta, Joaquín

doi:10.3390/s18082507

Cited by 39 publications

(31 citation statements)

References 18 publications

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“…Analyzing air pollution and, in particular, ozone levels at high temperatures, are major challenges for smart cities, as some authors have already warned [19][20][21]. Several works using Big Data Analytics in this context have been analyzed [11][12][13][14], but none of them addresses one of the most relevant problems in this Big Data era: the presence of sampling bias. To analyze the pollution issue in a reliable and efficient way, it is essential to detect and correct its effect.…”

Section: Discussionmentioning

confidence: 99%

“…A novel deep learning model based on Long Short Term Memory networks is presented in [13] to make predictions about air quality in smart cities. In [14], the authors use the existing sensor networks in smart cities to create and promote alternative pollution-free routes across cities depending on the level of pollution in each zone and apply the study carried out to Madrid (Spain).…”

Section: Similar Workmentioning

confidence: 99%

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Big-But-Biased Data Analytics for Air Quality

Borrajo

Cao

2020

Electronics

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Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘C based on 15 years of biased data are obtained.

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Section: Discussionmentioning

confidence: 99%

Section: Similar Workmentioning

confidence: 99%

Big-But-Biased Data Analytics for Air Quality

Borrajo

Cao

2020

Electronics

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“…The most common pollutants take into account for the Air Quality Index are CO 2 , NO 2 , O 3 , PM 2.5, PM 10, and SO 2 [1]. The air quality pollutants considered in this research work are the following: VOCs, CO, CO 2 , NOx, HC, NH 3 , and N 2 S. However, it is easy to add new sensors to the Air Quality Monitoring Unit and gathering other kind of pollutants.…”

Section: Background About Air Quality Pollutantsmentioning

confidence: 99%

“…Today, citizens have huge concerns about the quality of life in their cities, especially regarding the level of pollution. Air quality level is of great importance not only in planning our activities, but also in taking precautionary measures for our health [1]. Air pollution is one of the most important factors affecting the quality of life and the health of the increasing urban population of industrial societies [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Air Quality Monitoring Unit Using Cloudino and FIWARE Technologies

Gómez

Estrada-Esquivel

Rebollar

et al. 2019

MCA

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Smart City applications aim to improve the quality of life of citizens. Applying technologies of the Internet of Things (IoT) to urban environments is considered as a key of the development of smart cities. In this context, air pollution is one of the most important factors affecting the quality of life and the health of the increasing urban population of industrial societies. For this reason, it is essential to develop applications that allow citizens monitoring the concentration of pollutants and avoid places with high levels of pollution. Due to the increasing use of IoT in different areas, there are arising platforms which deal with the challenges IoT implies, such as FIWARE, which provides technologies to facilitate the development of IoT applications. In this paper, an Air Quality Monitoring Unit using Cloudino and Arduino devices and FIWARE technologies is presented. Through Cloudino and Arduino, the monitoring unit gather data from various sensors and transforms the data in a FIWARE data model. Then, the measurements are sent to the Orion Context Broker (OCB), which is a software component provided by FIWARE. The Orion Context Broker allows to manage and publish the data to be consumed by users and applications.

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“…The magnificent goals of smart cities include quality of life, sustainability, and development [1], which have already attracted a great deal of attention from researchers in an array of disciplines, such as urban planning [2][3][4], transportation [5][6][7], civil engineering [8], information science [9][10][11][12][13], surveying and mapping [14,15], commercial and logistics [16], energy [17,18], atmosphere and environment [19], society [20,21], tourists [22], governance [2,23], and industry in recent years. Although the proper definition of smart cities are still lacking [2], smart cities' goals are consistent to include smart living, smart people, smart environment, smart mobility, smart economy, smart governance [24], smart services, and smart infrastructure [25].…”

Section: Introductionmentioning

confidence: 99%

People-Centric Service Intelligence for Smart Cities

Hong

Geng

2019

Smart Cities

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In the era of big data, smart cities have become a promising prospect for governments, citizens, and industrials. Many ideas and their derived systems for smart cities depend on big data for achieving a goal of data intelligence. However, there is an urgent transformation trend from data intelligence to service intelligence in the vision of smart cities due to the living requirements of citizens. People-centric service intelligence in smart cities has to support the realization of people’s needs within urban and social domains. This paper introduces a concept of people-centric service intelligence, defines the level of it and its challenges in the aspect of infrastructure, human dynamics, human understanding and prediction, and the human–machine interface. Then, this paper proposes the theoretical framework and technical frameworks of people-centric service intelligence, and the service intelligence schemas for future construction of smart cities. It will be helpful for governments and industries to design people-centric service intelligence for improving the quality of life, the capabilities of good sustainability, and better development.

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Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks

Cited by 39 publications

References 18 publications

Big-But-Biased Data Analytics for Air Quality

Big-But-Biased Data Analytics for Air Quality

A Novel Air Quality Monitoring Unit Using Cloudino and FIWARE Technologies

People-Centric Service Intelligence for Smart Cities

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