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Scientific Article details

Title Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development
ID_Doc 38269
Authors Alsaig, A; Alagar, V; Chammaa, Z; Shiri, N
Title Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development
Year 2019
Published Sensors, 19, 11
DOI 10.3390/s19112430
Abstract Smart city is an emerging initiative for integrating Information and Communication Technologies (ICT) in effective ways to support development of smart cities with enhanced quality of life for its citizens through safe and secure context-aware services. Major technical challenges to realize smart cities include resource use optimization, service delivery without interruption at all times in all aspects, minimization of costs, and reduction of resource consumption. To address these challenges, new techniques and technologies are required for modeling and processing the big data generated and used through the underlying Internet of Things (IoT). To this end, we propose a data-centric approach to IoT in conceptualizing the things from a service-oriented perspective and investigate efficient ways to identify, integrate, and manage big data. The data-centric approach is expected to better support efficient management of data with complexities inherent in IoT-generated big data. Furthermore, it supports efficient and scalable query processing and reasoning techniques required in development of smart city applications. This article redresses the literature and contributes to the foundations of smart cities applications.
Author Keywords smart cities; big data; data model; databases; data integration; internet of things (IoT)
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
EID WOS:000472133300008
WoS Category Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation
Research Area Chemistry; Engineering; Instruments & Instrumentation
PDF https://www.mdpi.com/1424-8220/19/11/2430/pdf
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