Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

Binh, NTM; Binh, HTT; Van Linh, N; Yu, S

Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

Binh, NTM Binh, HTT Van Linh, N Yu, S

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Applied Intelligence, 2020, 50, (6), pp. 1889-1907
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Binh, NTM
dc.contributor.author	Binh, HTT
dc.contributor.author	Van Linh, N
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2020-12-25T00:03:11Z
dc.date.available	2020-12-25T00:03:11Z
dc.date.issued	2020-06-01
dc.identifier.citation	Applied Intelligence, 2020, 50, (6), pp. 1889-1907
dc.identifier.issn	0924-669X
dc.identifier.issn	1573-7497
dc.identifier.uri	http://hdl.handle.net/10453/144952
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Applied Intelligence
dc.relation.isbasedon	10.1007/s10489-019-01628-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things
dc.type	Journal Article
utslib.citation.volume	50
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-12-25T00:02:54Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	50
utslib.citation.issue	6

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.

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http://hdl.handle.net/10453/144952