FOG ANALYTICS - A SURVEY

Prabhu, CSR; Jan, T; Prasad, M; Varadarajan, V

FOG ANALYTICS - A SURVEY

Prabhu, CSR Jan, T Prasad, M

Varadarajan, V

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Publication Type:: Journal Article
Citation:: Malaysian Journal of Computer Science, 2020, 2020, (Special Issue 1), pp. 140-151
Issue Date:: 2020-11-27

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Field	Value	Language
dc.contributor.author	Prabhu, CSR
dc.contributor.author	Jan, T
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.contributor.author	Varadarajan, V
dc.date.accessioned	2021-06-15T05:48:32Z
dc.date.available	2021-06-15T05:48:32Z
dc.date.issued	2020-11-27
dc.identifier.citation	Malaysian Journal of Computer Science, 2020, 2020, (Special Issue 1), pp. 140-151
dc.identifier.issn	0127-9084
dc.identifier.uri	http://hdl.handle.net/10453/149525
dc.description.abstract	Fog computing has emerged as an essential alternative to the cloud. Fog computing is the future as it is nearer to the edge where actually the IOT devices and sensors are located. A Fog Server or Fog Node is located near to the IOT devices, connecting directly (wired or wireless) to them. The Fog Server has a functionality of fast accessibility to the data arising out of IOT devices or sensors, as against cloud server which may be located in data centers (near core Network Centers) located far away from the edge resulting in extreme delays in network transmission and latency, especially when the data is large volume as stream (or ‘Big Data’) arising out of IOT devices or sensors including cameras, etc. Real time response after completing the necessary Analytics on the data generated by IOT devices and sensors becomes critically essential for meeting the real time response requirements of critical applications such as in health care and transportation. What are the relevant techniques for Fog Analytics? In this paper we provide a brief survey of Fog Analytics techniques in stream data analytics, machine learning, deep learning techniques and also game theoretical adversarial learning.
dc.language	en
dc.relation.ispartof	Malaysian Journal of Computer Science
dc.relation.isbasedon	10.22452/mjcs.sp2020no1.10
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0807 Library and Information Studies
dc.title	FOG ANALYTICS - A SURVEY
dc.type	Journal Article
utslib.citation.volume	2020
utslib.for	0807 Library and Information Studies
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-06-15T05:48:31Z
pubs.issue	Special Issue 1
pubs.publication-status	Published
pubs.volume	2020
utslib.citation.issue	Special Issue 1

Abstract:

Fog computing has emerged as an essential alternative to the cloud. Fog computing is the future as it is nearer to the edge where actually the IOT devices and sensors are located. A Fog Server or Fog Node is located near to the IOT devices, connecting directly (wired or wireless) to them. The Fog Server has a functionality of fast accessibility to the data arising out of IOT devices or sensors, as against cloud server which may be located in data centers (near core Network Centers) located far away from the edge resulting in extreme delays in network transmission and latency, especially when the data is large volume as stream (or ‘Big Data’) arising out of IOT devices or sensors including cameras, etc. Real time response after completing the necessary Analytics on the data generated by IOT devices and sensors becomes critically essential for meeting the real time response requirements of critical applications such as in health care and transportation. What are the relevant techniques for Fog Analytics? In this paper we provide a brief survey of Fog Analytics techniques in stream data analytics, machine learning, deep learning techniques and also game theoretical adversarial learning.

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http://hdl.handle.net/10453/149525