High Density Real-Time Air Quality Derived Services from IoT Networks

Badii, Claudio; Bilotta, Stefano; Cenni, Daniele; Difino, Angelo; Nesi, Paolo; Paoli, Irene; Paolucci, Michela

doi:10.3390/s20185435

Cited by 13 publications

(12 citation statements)

References 30 publications

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“…, x i,T is an sequential observation of data at a one station S i , and x i,t = (t, lon, lat, v) represents one assessment from each station S i at a certain time step t. We can notice from the sample data that the range of raw information fluctuates greatly. Suppose, the [1,12] is the range features of the month, whereas the limit of PM 2.5 values is (0, 210]. As a result, we measure the informative features so that all values fall between 0 and 1.…”

Section: Data Set Descriptionmentioning

confidence: 99%

“…A black box strategy, including the artificial intelligent algorithm, is a reasonable concept and an auspicious way to predict the spatiotemporal assessment parameter in such scenarios. Furthermore, Badli et al [12] presented an effective parallel machine learning model to address this challenge in order to regulate the appropriate spatiotemporal anisotropy parameters.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting

Zulqarnain¹,

Ghazali²,

Shah³

et al. 2022

Computers, Materials &Amp; Continua

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Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM 2.5 ) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM 2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced deep learning approaches have been proposed to forecast Particulate Matter (PM 2.5 ). Recurrent neural network (RNN) is one of the popular deep learning architectures which is widely employed in PM 2.5 concentration forecasting. In this research, we proposed a Two-State Gated Recurrent Unit (TS-GRU) for monitoring and estimating the PM 2.5 concentration forecasting system. The proposed algorithm is capable of considering both spatial and temporal hidden affecting elements spontaneously. We tested our model using data from daily PM 2.5 dimensions taken in the contactual southeast area of the United States in 2009. In the studies, three evaluation matrices were utilized to compare the overall performance of each algorithm: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results revealed that our proposed TS-GRU model outperformed compared to the other deep learning approaches in terms of forecasting performance.

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Section: Data Set Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting

Zulqarnain¹,

Ghazali²,

Shah³

et al. 2022

Computers, Materials &Amp; Continua

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“…The sensors collect data from the fields of transport, healthcare, social networks and media, limited resources (especially air) and the government. Data are aggregated through IoT applications, then analyzed and used in city management processes [65].…”

Section: Helsinkimentioning

confidence: 99%

“…The city of Helsinki publicly shares the collected data from the sensory network on city dashboards. In cooperation with Nokia, an MEC (mobile edge computing) network with object tracking, camera surveillance and video analysis applications has been used since 2016 [65,66].…”

Section: Helsinkimentioning

confidence: 99%

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Advanced Technologies and Their Use in Smart City Management

2021

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Building Smart City management concepts is based on the implementation and use of advanced technologies. The primary impulse for writing the article was the ambition to identify the current advanced technologies of Smart City management. The aim of the article is to propose a general model for the implementation of advanced technologies for Smart City management, based on the knowledge gained from the analysis of literature and case studies. In order to fulfill the set goal, it is necessary to obtain answers to two research questions. The findings were obtained through a secondary analysis of the literature, i.e., relevant articles from the scientific databases Web of Science and Scopus analysis of case studies of the best Smart Cities practices. According to the Smart City Index 2020 and IESE Cities in Motion, the leaders among the Smart Cities are Singapore and London, followed by Helsinki. In addition to the analyses, the article also uses methods of summarization, comparison, creativity, logic, induction and deduction. Smart Cities use 12 identified advanced technologies in their practice. Strategic management in Singapore, London and Helsinki adapts technology to the needs and requirements of its citizens, thus connecting the technological aspect with the managerial and social aspects. The contributions of the work include results for fellow researchers and a model for strategic management of new Smart Cities. The results of the article provide fellow researchers with the findings of a secondary analysis of relevant articles, from which they can draw when writing their own publications without the need for time-consuming search of the articles about this topic in databases. The general model of implementation of advanced technologies serves as a basis for strategic management of new Smart Cities that want to implement a technological base and at the same time do not want to forget the managerial and social aspects. Testing the model in practice with a new Slovak Smart City is part of future research activities.

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