scholarly journals Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and Scientometric Analysis

2019 ◽  
Vol 11 (23) ◽  
pp. 6648 ◽  
Author(s):  
Li Zhao ◽  
Zhi-ying Tang ◽  
Xin Zou
Keyword(s):  
Big Data ◽  
Smart City ◽  
Applied Research ◽  
Citation Index ◽  
Smart Cities ◽  
Knowledge Bases ◽  
Scientometric Analysis ◽  
Knowledge Mapping ◽  
Knowledge Domain ◽  
Research Frontiers

As urbanization continues to accelerate, the number of cities and their growing populations have created problems, such as the congestion and noise related to transportation, the pollution from industry, and the difficulty of disposing of garbage. An emerging urban strategy is to make use of digital technologies and big data to help improve the quality of life of urban residents. In the past decade, more and more researchers have studied smart cities, and the number of literature in this field grows rapidly, making it “big data”. With the aim of better understanding the contexts of smart-city research, including the distribution of topics, knowledge bases, and the research frontiers in the field, this paper is based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) in the Web of Science (WoS) Core Collection, and the method used is that of comprehensive scientometric analysis and knowledge mapping in terms of diversity, time slicing, and dynamics, using VOSviewer and CiteSpace to study the literature in the field. The main research topics can be divided into three areas—“the concepts and elements of the smart city”, “the smart city and the Internet of Things”, and “the smart city of the future”—through document co-citation analysis. There are four key directions—“research objectives and development-strategy research”, “technical-support research”, “data-processing and applied research”, and “management and applied research”—analyzed using keywords co-occurrence. Finally, the research frontiers are urban-development, sustainable cities, cloud computing, artificial intelligence, integration, undertaken through keyword co-occurrence analysis.

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

Using a distributed deep learning algorithm for analyzing big data in smart cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Abdelfattah Amamra ◽  
Okba kazar
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Storage ◽  
Smart City ◽  
Smart Cities ◽  
Smart Environment ◽  
Data Systems ◽  
Content Type ◽  
Big Data Systems

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

Smart and Connected Cities Through Technologies

2019 ◽  
pp. 1-24 ◽  
Author(s):  
Vrushali Gajanan Kadam ◽  
Sharvari Chandrashekhar Tamane ◽  
Vijender Kumar Solanki
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Smart City ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Crowd Sensing ◽  
The World ◽  
Important Challenge ◽  
Mobile Crowd

The world is growing and energy conservation is a very important challenge for the engineering domain. The emergence of smart cities is one possible solution for the same, as it claims that energy and resources are saved in the smart city infrastructure. This chapter is divided into five sections. Section 1 gives the past, present, and future of the living style. It gives the representation from rural, urban, to smart city. Section 2 gives the explanations of four pillars of big data, and through grid, a big data analysis is presented in the chapter. Section 3 started with the case study on smart grid. It comprises traffic congestion and their prospective solution through big data analytics. Section 4 starts from the mobile crowd sensing. It discusses a good elaboration on crowd sensing whereas Section 5 discusses the smart city approach. Important issues like lighting, parking, and traffic were taken into consideration.

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

scholarly journals Managing Large Amounts of Data Generated by a Smart City Internet of Things Deployment

2016 ◽  
Vol 12 (4) ◽  
pp. 22-42 ◽  
Author(s):  
Jorge Lanza ◽  
Pablo Sotres ◽  
Luis Sánchez ◽  
Jose Antonio Galache ◽  
Juan Ramón Santana ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Communication Technologies ◽  
Added Value ◽  
Amount Of Information ◽  
Information And Communication ◽  
The City

The Smart City concept is being developed from a lot of different axes encompassing multiple areas of social and technical sciences. However, something that is common to all these approaches is the central role that the capacity of sharing information has. Hence, Information and Communication Technologies (ICT) are seen as key enablers for the transformation of urban regions into Smart Cities. Two of these technologies, namely Internet of Things and Big Data, have a predominant position among them. The capacity to “sense the city” and access all this information and provide added-value services based on knowledge derived from it are critical to achieving the Smart City vision. This paper reports on the specification and implementation of a software platform enabling the management and exposure of the large amount of information that is continuously generated by the IoT deployment in the city of Santander.

scholarly journals Whose smart city? : A framework and discussion guide for planning open and inclusive urban digital experiments

2021 ◽  
Author(s):  
Steven Coutts
Keyword(s):  
Big Data ◽  
Citizen Participation ◽  
Smart City ◽  
Smart Cities ◽  
Popular Literature ◽  
Research Approach ◽  
New Paradigm ◽  
Urban Informatics ◽  
Planning Profession ◽  
Action Research Approach

‘Smart cities’ represent the integration of ‘big data’ collected via networked cameras, sensors, and meters into the urban fabric with the overarching goal of making infrastructure more efficient and improving citizens’ lives. While data has been used to support planning efforts for decades, this new paradigm of ‘urban informatics’ means that planning will increasingly be driven by data. However, the planning profession is still grappling with how existing practices might need to adapt to tackle the challenges of planning in the smart city. Accordingly, there is a gap in terms of educational resources on smart cities aimed at planning professionals. Through an action research approach involving a review of recent academic and popular literature on smart cities, this project synthesizes a set of ‘best practices’ and proposes a discussion guide for planning professionals to learn about the implications for their practice in a world where big data shapes our cities. Keywords: smart cities, urban informatics, planning ethics, Big Data, citizen participation

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

scholarly journals PROTECTION OF PRIVACY AND PERSONAL DATA IN THE BIG DATA ENVIRONMENT OF SMART CITIES

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 181-186
Author(s):  
A. Denker
Keyword(s):  
Big Data ◽  
Smart City ◽  
Personal Information ◽  
Smart Cities ◽  
Personal Data ◽  
Privacy And Security ◽  
Personal Data Protection ◽  
Security Issues ◽  
Data Environment ◽  
Bifocal Lenses

Abstract. The project of smart cities has emerged as a response to the challenges of twenty-first- century urbanization. Solutions to the fundamental conundrum of cities revolving around efficiency, convenience and security keep being sought by leveraging technology. Notwithstanding all the conveniences furnished by a smart city to all the citizens, privacy of a citizen is intertwined with the benefits of a smart city. The development processes which overlook privacy and security issues have left many of the smart city applications vulnerable to non-conventional security threats and susceptible to numerous privacy and personal data spillage risks. Among the challenges the smart city initiatives encounter, the emergence of the smartphone-big data-the cloud coalescence is perhaps the greatest, from the viewpoint of privacy and personal data protection. As our cities are getting digitalized, information comprising citizens' behavior, choices, and mobility, as well as their personal assets are shared over smartphone-big data-the cloud coalescences, thereby expanding cyber-threat surface and creating different security concerns. This coalescence refers to the practices of creating and analyzing vast sets of data, which comprise personal information. In this paper, the protection of privacy and personal data issues in the big data environment of smart cities are viewed through bifocal lenses, focusing on social and technical aspects. The protection of personal data and privacy in smart city enterprises is treated as a socio-technological operation where various actors and factors undertake different tasks. The article concludes by calling for novel developments, conceptual and practical changes both in technological and social realms.

scholarly journals Study and Research on IoT and Big Data Analysis for Smart City Development

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Haixia Yu ◽  
Ion Cosmin Mihai ◽  
Anand Srivastava
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Data Generation ◽  
City Development ◽  
Smart Meters ◽  
Smart Systems ◽  
Smart Parking ◽  
Iot Devices ◽  
Time Required

With the development of smart meters, like Internet of Things (IoT), various kinds of electronic devices are equipped with each smart city. The several aspects of smart cities are accessible and these technologies enable us to be smarter. The utilization of the smart systems is very quick and valuable source to fulfill the requirement of city development. There are interconnection between various IoT devices and huge amount of data is generated when they communicate each other over the internet. It is very challenging task to effectively integrate the IoT services and processing big data. Therefore, a system for smart city development is proposed in this paper which is based on the IoT utilizing the analytics of big data. A complete system is proposed which includes various types of IoT-based smart systems like smart home, vehicular networking, and smart parking etc., for data generation. The Hadoop ecosystem is utilized for the implementation of the proposed system. The evaluation of the system is done in terms of throughput and processing time. The proposed technique is 20% to 65% better than the existing techniques in terms of time required for processing. In terms of obtained throughput, the proposed technique outperforms the existing technique by 20% to 60%.

Deconstructing Smart Cities

Big Data ◽  
2016 ◽  
pp. 1957-1969
Author(s):  
Michael Batty
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Smart City ◽  
New Technologies ◽  
Smart Cities ◽  
Public Realm ◽  
The Public ◽  
Important Challenge ◽  
Urban Problems ◽  
The City

This chapter defines the smart city in terms of the process whereby computers and computation are being embedded into the very fabric of the city itself. In short, the smart city is the automated city where the goal is to improve the efficiency of how the city functions. These new technologies tend to improve the performance of cities in the short term with respect to how cities function over minutes, hours or days rather than over years or decades. After establishing definitions and context, the author then explores questions of big data. One important challenge is to synthesize or integrate different data about the city's functioning and this provides an enormous challenge which presents many obstacles to producing coherent solutions to diverse urban problems. The chapter augments this argument with ideas about how the emergence of widespread computation provides a new interface to the public realm through which citizens might participate in rather fuller and richer ways than hitherto, through interactions in various kinds of decision-making about the future city. The author concludes with some speculations as to how the emerging science of smart cities fits into the wider science of cities.

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