An Optimized IoT-Enabled Big Data Analytics Architecture for Edge-Cloud Computing

Babar, M; Jan, MA; He, X; Tariq, MU; Mastorakis, S; Alturki, R

An Optimized IoT-Enabled Big Data Analytics Architecture for Edge-Cloud Computing

Babar, M Jan, MA He, X

Tariq, MU Mastorakis, S Alturki, R

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2023, 10, (5), pp. 3995-4005
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Babar, M
dc.contributor.author	Jan, MA
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.contributor.author	Tariq, MU
dc.contributor.author	Mastorakis, S
dc.contributor.author	Alturki, R
dc.date.accessioned	2023-03-20T02:22:28Z
dc.date.available	2023-03-20T02:22:28Z
dc.date.issued	2023-03-01
dc.identifier.citation	IEEE Internet of Things Journal, 2023, 10, (5), pp. 3995-4005
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/167699
dc.description.abstract	The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of the Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: 1) IoT-edge and 2) cloud processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. An optimized yet another resource negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic data set using Apache Spark. The comparative analysis is decorated with the existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2022.3157552
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	An Optimized IoT-Enabled Big Data Analytics Architecture for Edge-Cloud Computing
dc.type	Journal Article
utslib.citation.volume	10
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/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
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	2023-03-20T02:22:27Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	5

Abstract:

The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of the Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: 1) IoT-edge and 2) cloud processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. An optimized yet another resource negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic data set using Apache Spark. The comparative analysis is decorated with the existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.

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http://hdl.handle.net/10453/167699