Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things

Liu, X; Zhao, S; Liu, A; Xiong, N; Vasilakos, AV

Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things

Liu, X Zhao, S Liu, A Xiong, N Vasilakos, AV

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Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2019, 92 pp. 1142 - 1156
Issue Date:: 2019-03-01

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Field	Value	Language
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Zhao, S	en_US
dc.contributor.author	Liu, A	en_US
dc.contributor.author	Xiong, N	en_US
dc.contributor.author	Vasilakos, AV https://orcid.org/0000-0003-1902-9877	en_US
dc.date.accessioned	2020-04-14T04:41:42Z
dc.date.available	2020-04-14T04:41:42Z
dc.date.issued	2019-03-01	en_US
dc.identifier.citation	Future Generation Computer Systems, 2019, 92 pp. 1142 - 1156	en_US
dc.identifier.issn	0167-739X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139982
dc.description.abstract	© 2017 Elsevier B.V. Internet of Things will serve communities across the different domains of life. Tracking mobile targets is one important system engineering application in IOT, and the resource of embedded devices and objects working under IoT implementation are constrained. Thus, building a scheme to make full use of energy is key issue for mobile target tracking applications. To achieve both energy efficiency and high monitoring performance, an effective Knowledge-aware Proactive Nodes Selection (KPNS) system is proposed in this paper. The innovations of KPNS are as follows: 1) the number of proactive nodes are dynamically adjusted based on prediction accuracy of target trajectory. If the prediction accuracy is high, the number of proactive nodes in the non-main predicted area will be decreased. If prediction accuracy of moving trajectory is low, large number of proactive nodes will be selected to enhance monitoring quality. 2) KPNS takes full advantage of energy to further enhance target tracking performance by properly selecting more proactive nodes in the network. We evaluated the efficiency of KPNS with both theory analysis and simulation based experiments. The experimental results demonstrate that compared with Probability-based target Prediction and Sleep Scheduling strategy (PPSS), KPNS scheme improves the energy efficiency by 60%, and can reduce target missing rate and tracking delay to 66%, 75% respectively.	en_US
dc.relation.ispartof	Future Generation Computer Systems	en_US
dc.relation.isbasedon	10.1016/j.future.2017.07.022	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Distributed Computing	en_US
dc.title	Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things	en_US
dc.type	Journal Article
utslib.citation.volume	92	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	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
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	92	en_US

Abstract:

© 2017 Elsevier B.V. Internet of Things will serve communities across the different domains of life. Tracking mobile targets is one important system engineering application in IOT, and the resource of embedded devices and objects working under IoT implementation are constrained. Thus, building a scheme to make full use of energy is key issue for mobile target tracking applications. To achieve both energy efficiency and high monitoring performance, an effective Knowledge-aware Proactive Nodes Selection (KPNS) system is proposed in this paper. The innovations of KPNS are as follows: 1) the number of proactive nodes are dynamically adjusted based on prediction accuracy of target trajectory. If the prediction accuracy is high, the number of proactive nodes in the non-main predicted area will be decreased. If prediction accuracy of moving trajectory is low, large number of proactive nodes will be selected to enhance monitoring quality. 2) KPNS takes full advantage of energy to further enhance target tracking performance by properly selecting more proactive nodes in the network. We evaluated the efficiency of KPNS with both theory analysis and simulation based experiments. The experimental results demonstrate that compared with Probability-based target Prediction and Sleep Scheduling strategy (PPSS), KPNS scheme improves the energy efficiency by 60%, and can reduce target missing rate and tracking delay to 66%, 75% respectively.

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http://hdl.handle.net/10453/139982