Paper Info
Title
Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making.
Abstract
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.
Year
DOI
Venue
2017
10.1155/2017/9429676
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Field
DocType
Volume
Data science,Data architecture,Architecture,Data processing,World Wide Web,Real-time data,Computer science,Smart city,Data management,Big data,Test data generation,Distributed computing
Journal
2017
ISSN
Citations 
PageRank 
1530-8669
9
0.56
References 
Authors
8
3
Name
Order
Citations
PageRank
Bhagya Nathali Silva1102.26
Murad Khan215022.14
Kijun Han329456.26