P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

Usman, M; Jan, MA; He, X; Chen, J

P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

Usman, M

Jan, MA He, X

Chen, J

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Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Areas in Communications, 2019, 37 (6), pp. 1222 - 1230
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Usman, M https://orcid.org/0000-0003-2165-4575	en_US
dc.contributor.author	Jan, MA	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Chen, J	en_US
dc.date.available	2021-08-12T19:00:57Z
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	IEEE Journal on Selected Areas in Communications, 2019, 37 (6), pp. 1222 - 1230	en_US
dc.identifier.issn	0733-8716	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137423
dc.description.abstract	© 1983-2012 IEEE. The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP170100136
dc.relation	http://purl.org/au-research/grants/arc/LP140100816
dc.relation.ispartof	IEEE Journal on Selected Areas in Communications	en_US
dc.relation.isbasedon	10.1109/JSAC.2019.2904349	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	37	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
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
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
utslib.copyright.status	open_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	37	en_US

Abstract:

© 1983-2012 IEEE. The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.

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http://hdl.handle.net/10453/137423