Machine learning applications for fog computing in IoT: A survey

Mousavi, M; Rezazadeh, J; Sianaki, OA

Machine learning applications for fog computing in IoT: A survey

Mousavi, M Rezazadeh, J

Sianaki, OA

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Publisher:: INDERSCIENCE ENTERPRISES LTD
Publication Type:: Journal Article
Citation:: International Journal of Web and Grid Services, 2021, 17, (4), pp. 293-320
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Mousavi, M
dc.contributor.author	Rezazadeh, J https://orcid.org/0000-0002-6851-0117
dc.contributor.author	Sianaki, OA
dc.date.accessioned	2022-05-14T03:55:44Z
dc.date.available	2022-05-14T03:55:44Z
dc.date.issued	2021-01-01
dc.identifier.citation	International Journal of Web and Grid Services, 2021, 17, (4), pp. 293-320
dc.identifier.issn	1741-1106
dc.identifier.issn	1741-1114
dc.identifier.uri	http://hdl.handle.net/10453/157342
dc.description.abstract	Today, internet of things (IoT) has become an important paradigm. Everyday increasing number of IoT applications and services emerge. Smart devices connected by the IoT generate significant amounts of data. Analysis IoT sensor data using machine learning algorithms is a key to achieve useful information for prediction, classification, data association and data conceptualisation. Offloading input data to cloud servers leads to increased communication costs. Undertaking data analytics at the network edge using fog computing enables the rapid processing of incoming data for real-time response. In this paper, we examine the results of using different machine learning algorithms on fog nodes based on existing research. These results are low latency, high accuracy and low bandwidth. Also, this work presents the current fog computing architecture which consists of different layers that distribute computing, storage, control and networking and finally we investigate the challenges and open issues related to the deployment of machine learning on fog nodes.
dc.language	English
dc.publisher	INDERSCIENCE ENTERPRISES LTD
dc.relation.ispartof	International Journal of Web and Grid Services
dc.relation.isbasedon	10.1504/IJWGS.2021.118395
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	Machine learning applications for fog computing in IoT: A survey
dc.type	Journal Article
utslib.citation.volume	17
utslib.for	0805 Distributed Computing
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	2022-05-14T03:55:42Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	4

Abstract:

Today, internet of things (IoT) has become an important paradigm. Everyday increasing number of IoT applications and services emerge. Smart devices connected by the IoT generate significant amounts of data. Analysis IoT sensor data using machine learning algorithms is a key to achieve useful information for prediction, classification, data association and data conceptualisation. Offloading input data to cloud servers leads to increased communication costs. Undertaking data analytics at the network edge using fog computing enables the rapid processing of incoming data for real-time response. In this paper, we examine the results of using different machine learning algorithms on fog nodes based on existing research. These results are low latency, high accuracy and low bandwidth. Also, this work presents the current fog computing architecture which consists of different layers that distribute computing, storage, control and networking and finally we investigate the challenges and open issues related to the deployment of machine learning on fog nodes.

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http://hdl.handle.net/10453/157342