When things matter: A survey on data-centric internet of things

Qin, Y; Sheng, QZ; Falkner, NJG; Dustdar, S; Wang, H; Vasilakos, AV

When things matter: A survey on data-centric internet of things

Qin, Y Sheng, QZ Falkner, NJG Dustdar, S Wang, H Vasilakos, AV

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Journal of Network and Computer Applications, 2016, 64, pp. 137-153
Issue Date:: 2016-04-01

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Field	Value	Language
dc.contributor.author	Qin, Y
dc.contributor.author	Sheng, QZ
dc.contributor.author	Falkner, NJG
dc.contributor.author	Dustdar, S
dc.contributor.author	Wang, H
dc.contributor.author	Vasilakos, AV
dc.date.accessioned	2022-08-11T22:53:34Z
dc.date.available	2022-08-11T22:53:34Z
dc.date.issued	2016-04-01
dc.identifier.citation	Journal of Network and Computer Applications, 2016, 64, pp. 137-153
dc.identifier.issn	1084-8045
dc.identifier.issn	1095-8592
dc.identifier.uri	http://hdl.handle.net/10453/159982
dc.description.abstract	With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.
dc.language	English
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	Journal of Network and Computer Applications
dc.relation.isbasedon	10.1016/j.jnca.2015.12.016
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	When things matter: A survey on data-centric internet of things
dc.type	Journal Article
utslib.citation.volume	64
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-11T22:53:33Z
pubs.publication-status	Published
pubs.volume	64

Abstract:

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.

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http://hdl.handle.net/10453/159982