Towards Sustainable Smart City by Particulate Matter Prediction Using Urban Big Data, Excluding Expensive Air Pollution Infrastructures

Honarvar, Ali Reza; Sami, Ashkan

doi:10.1016/j.bdr.2018.05.006

Cited by 72 publications

(44 citation statements)

References 17 publications

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“…It also enables the study of the traffic produced by realistic M2M components in smart cities environments. Recently, (Honarvar and Sami 2019) used real urban dataset collected and extracted from multiple sources in the city of Aarhus, Denmark, to develop a prediction of particulate matter model in the city. The data collected are related to urban buildings, road traffic, air pollution, weathercasts, and points of interest (POI).…”

Section: City Analyticsmentioning

confidence: 99%

Smart Cities Data: Framework, Applications, and Challenges

Bilal

Usmani

Tayyab

et al. 2020

Handbook of Smart Cities

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Recent technological developments and the availability of enormous amounts of real-time data have played a vital role in the expansion, evolution, and success of smart city projects. Smart data can be used in a variety of smart city applications, but difficulties in managing such data are pushing smart cities toward the adoption of data management frameworks. Many studies have brought into focus the importance of these frameworks as they combine data collection, processing, analysis, management, and visualization and provide privacy and security features for different smart city applications, i.e., transportation, to promote a better quality of life. This chapter highlights key components of the data management framework, reviews various smart city applications, and discusses privacy and security challenges associated with smart city data. From the perspective of data frameworks, it is seen that the data used in smart city applications is unstructured coming from heterogeneous sources, i.e., sensors and social media, besides others. Therefore, the collection, processing, analysis, management, and visualization of such data are challenging. To perform these tasks, recent technologies, i.e., Internet of Things (IoT), sensor networks, machine learning, etc., have been used. Moreover, the use of smart data for smart government and governance provides several facilities for the public and business. The smart data is revolutionizing the daily communication of users along with their mode of transportation by introducing Social IoT (SIoT) and autonomous vehicles. Lastly, the challenges related to privacy and security of the data in smart cities that needed to be addressed are highlighted. This chapter will guide academics and enterprises to progress in data management framework and its applications in smart cities in the near future.

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Section: City Analyticsmentioning

confidence: 99%

Smart Cities Data: Framework, Applications, and Challenges

Bilal

Usmani

Tayyab

et al. 2020

Handbook of Smart Cities

View full text Add to dashboard Cite

show abstract

“…Consider two ACWs referring to the same time instant t, but of different sizes, C Here we suppose that the agent has previously collected enough information to calculate both ACWs. The agent evaluates the estimate of information t by simulating its absence using both C (4) t , C (7) t . This results in two different estimates that are compared to the real value.…”

Section: ) Evaluating Dynamic Size Acwsmentioning

confidence: 99%

“…Wide dissemination of environmental information such as temperature, humidity, CO 2 enables government agencies to map cities and regions to provide accurate information at any time and everywhere in parts of the environment that are not sufficiently covered by sensors. This could help to assess important information such as pollution [4], hydrological forecasting [5] or traffic estimation [6]. For these reasons, it is necessary to conceive effective solutions that enable leveraging the potential of data perceived through a wide range of sensing devices.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

2020

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The concept of Smart City has spread as a solution to ensure better access to information and services to citizens, but also as a means to reduce the environmental footprint of cities. To this end, a continuous and wide observation of the environment is necessary to analyze information that enables government bodies to act on the environment appropriately. Moreover, a diffused acquisition of information requires adequate infrastructure and proper devices, which results in relevant installation and maintenance costs. Our proposal enables reducing the number of necessary sensors to be deployed while ensuring that information is available at any time and anywhere. We present the HybridIoT system to cope with the lack of environmental information in the urban context through an estimation technique that integrates heterogeneous data acquired from some different sensors. HybridIoT can be deployed in large-scale contexts and ensures data accessibility even if devices enter or leave the system at any time and everywhere. We compare the results to those obtained by state-of-the-art techniques to assess the validity of our proposal, in particular concerning the properties of openness, large-scale, and heterogeneity, of primary importance in the context of the development of systems to be deployed in the smart city context.

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“…Each district is often dominated by commercial activities, green spaces, long/medium distance streets, industrial areas, etc. In [ 20 ], a study was carried out on AQI (Air Quality Index) considering the various pollutants and bearing in mind the points of interest in a Smart City, where each sensor was positioned, although in reality the research was based on the quality of AQI prediction from the type of sensors used and no real study was made to correlate the type of pollutant that most affects the calculation of AQI and type of area of the city (e.g., commercial, residential, green, city center, RTZ (Restricted Traffic Zone). In [ 21 ], a comparison among the AQI and the different city contexts was estimated, and the study was limited to roadside and city background situations.…”

Section: Introductionmentioning

confidence: 99%

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

Badii

Bilotta

Cenni

et al. 2020

Sensors

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In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices’ dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

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Towards Sustainable Smart City by Particulate Matter Prediction Using Urban Big Data, Excluding Expensive Air Pollution Infrastructures

Cited by 72 publications

References 17 publications

Smart Cities Data: Framework, Applications, and Challenges

Smart Cities Data: Framework, Applications, and Challenges

A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

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

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