Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making
The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly chal...
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| Veröffentlicht in: | Wireless communications and mobile computing 2017-01, Vol.2017 (2017), p.1-12 |
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| container_title | Wireless communications and mobile computing |
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| creator | Nathali Silva, Bhagya Han, Kijun Khan, Murad |
| description | The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture. |
| doi_str_mv | 10.1155/2017/9429676 |
| format | Article |
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Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2017/9429676</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Architecture ; Big Data ; Communication ; Community development ; Data analysis ; Data collection ; Data management ; Data processing ; Datasets ; Decision analysis ; Decision making ; Energy consumption ; Interest groups ; Internet of Things ; Optimization techniques ; Performance enhancement ; Real time ; Researchers ; Smart cities ; Social research ; Surveillance ; Traffic congestion ; Wireless networks</subject><ispartof>Wireless communications and mobile computing, 2017-01, Vol.2017 (2017), p.1-12</ispartof><rights>Copyright © 2017 Bhagya Nathali Silva et al.</rights><rights>Copyright © 2017 Bhagya Nathali Silva et al. 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| subjects | Architecture Big Data Communication Community development Data analysis Data collection Data management Data processing Datasets Decision analysis Decision making Energy consumption Interest groups Internet of Things Optimization techniques Performance enhancement Real time Researchers Smart cities Social research Surveillance Traffic congestion Wireless networks |
| title | Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making |
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