scholarly journals Mobile information system for monitoring the spread of viruses in smart cities

2020 ◽  
Vol 8 ◽  
pp. 65-70
Author(s):  
Oleksii Duda ◽  
◽  
Liliana Dzhydzhora ◽  
Oleksandr Matsiuk ◽  
Andrii Stanko ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Smart City ◽  
Smart Cities ◽  
Viral Disease ◽  
Viral Diseases ◽  
Treatment Protocols ◽  
Emergency Situations ◽  
Treatment And Prevention ◽  
Multi Level

The concept of creating a multi-level mobile personalized system for fighting viral diseases, in particular Covid-19, was developed. Using the integration of the Internet of Things, Cloud Computing and Big Data technologies, the system involves a combination of two architectures: client-server and publication-subscription. The advantage of the system is the permanent help with viral diseases, namely on communication, information, and medical stages. The smart city concept in the context of viral disease control focuses on the application of Big Data analysis methods and the improvement of forecasting procedures and emergency treatment protocols. Using different technologies, cloud server stores the positioning data obtained from different devices, and the application accesses API to display and analyze the positioning data in real time. Due to the technologies combination, internal and external positioning can be used with a certain accuracy degree, being useful for various medical and emergency situations and analysis and the following processing by other smart city information systems. The result of the given investigation is the development of the conceptual model of multi-level mobile personalized health status monitoring system used for intellectual data analysis, prediction, treatment and prevention of viral diseases such as Covid-19 in modern “smart city”.

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.

scholarly journals DENDROGRAM CLUSTERING FOR 3D DATA ANALYTICS IN SMART CITY

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 247-253 ◽  
Author(s):  
S. Azri ◽  
U. Ujang ◽  
A. Abdul Rahman
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Smart City ◽  
Data Analytics ◽  
City Planning ◽  
Collective Intelligence ◽  
Smart Cities ◽  
Three Dimensional ◽  
Complex Data ◽  
Social Infrastructure

<p><strong>Abstract.</strong> Smart city is a connection of physical and social infrastructure together with the information technology to leverage the collective intelligence of the city. Cities will build huge data centres. These data are collected from sensors, social media, and legacy data sources. In order to be smart, cities needs data analysis to identify infrastructure that needs to be improved, city planning and predictive analysis for citizen safety and security. However, no matter how much smart city focus on the updated technology, data do not organize themselves in a database. Such tasks require a sophisticated database structure to produce informative data output. Furthermore, increasing number of smart cities and generated data from smart cities contributes to current phenomenon called big data. These large and complex data collections would be difficult to process using regular database management tools or traditional data processing applications. There are multiple challenges for big data, including visualization, mining, analysis, capture, storage, search, and sharing. Efficient data analysis mechanisms are necessary to search and extract valuable patterns and knowledge through the big data of smart cities. In this paper, we present a technique of three-dimensional data analytics using dendrogram clustering approach. Data will be organized using this technique and several output and analyses are carried out to proof the efficiency of the structure for three – dimensional data analytics in smart city.</p>

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.

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.

Big Data Analysis for Retrofit Projects in Smart Cities

2019 ◽  
Author(s):  
Haiyan Xie ◽  
Wei Shi ◽  
Harshit Choudhary ◽  
Hanliang Fu ◽  
Xiaotong Guo
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Smart Cities ◽  
Big Data Analysis

A value of civic voices for smart city: A big data analysis of civic queries posed by Seoul citizens

Cities ◽  
2021 ◽  
Vol 108 ◽  
pp. 102941 ◽  
Author(s):  
Byungjun Kim ◽  
Minjoo Yoo ◽  
Keon Chul Park ◽  
Kyeo Re Lee ◽  
Jang Hyun Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Smart City ◽  
Big Data Analysis ◽  
A Value
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