A Distributed and Anonymous Data Collection Framework Based on Multilevel Edge Computing Architecture

Usman, M; Jan, MA; Jolfaei, A; Xu, M; He, X; Chen, J

A Distributed and Anonymous Data Collection Framework Based on Multilevel Edge Computing Architecture

Usman, M Jan, MA Jolfaei, A Xu, M

He, X

Chen, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2020, 16, (9), pp. 6114-6123
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Usman, M
dc.contributor.author	Jan, MA
dc.contributor.author	Jolfaei, A
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.contributor.author	Chen, J
dc.date.accessioned	2020-10-15T10:37:03Z
dc.date.available	2020-10-15T10:37:03Z
dc.date.issued	2020-09-01
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2020, 16, (9), pp. 6114-6123
dc.identifier.issn	1551-3203
dc.identifier.issn	1941-0050
dc.identifier.uri	http://hdl.handle.net/10453/143279
dc.description.abstract	© 2005-2012 IEEE. Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Industrial Informatics
dc.relation.isbasedon	10.1109/TII.2019.2952645
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Electrical & Electronic Engineering
dc.title	A Distributed and Anonymous Data Collection Framework Based on Multilevel Edge Computing Architecture
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-15T10:36:53Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	9

Abstract:

© 2005-2012 IEEE. Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/143279