Internet of Mission-Critical Things: Human and Animal Classification-A Device-Free Sensing Approach

Zhong, Y; Dutkiewicz, E; Yang, Y; Zhu, X; Zhou, Z; Jiang, T

Internet of Mission-Critical Things: Human and Animal Classification-A Device-Free Sensing Approach

Zhong, Y

Dutkiewicz, E

Yang, Y

Zhu, X

Zhou, Z Jiang, T

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Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2018, 5 (5), pp. 3369 - 3377
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Zhong, Y https://orcid.org/0000-0002-9309-3407	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-7439-2156	en_US
dc.contributor.author	Zhu, X https://orcid.org/0000-0002-5814-394X	en_US
dc.contributor.author	Zhou, Z	en_US
dc.contributor.author	Jiang, T	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	IEEE Internet of Things Journal, 2018, 5 (5), pp. 3369 - 3377	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123991
dc.description.abstract	© 2014 IEEE. The well-known Internet of Things (IoT) is recently being considered for critical missions, such as search and rescue, surveillance, and border patrol. One of the most critical issues that these applications are currently facing is how to correctly distinguish between human and animal targets in a cost-effective way. In this paper, we present a relatively low-cost, but robust approach that uses a combination of device-free sensing (DFS) and machine-learning technologies to tackle this issue. In order to validate the feasibility of the presented approach, a variety of data is collected in a cornfield using impulse-radio ultra-wideband (IR-UWB) transceivers. These data are then used to investigate the influence of different statistical properties of the radio-frequency (RF) signal on the accuracy of human/animal target classification. Based on the probability density function of different statistical properties, two distinguishing features for target classification are found, namely, standard deviation and root mean spread delay spread. Using them, the impact on the classification accuracy due to different classifiers, number of training samples, and different values of signal-To-noise ratio is extensively verified. Even with the worst case, the classification accuracy of the system is still better than 91% in terms of distinguishing between human and animal targets (including goats and dogs), which indicates that the presented approach has a great potential to be deployed in the near future.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE160101032
dc.relation.ispartof	IEEE Internet of Things Journal	en_US
dc.relation.isbasedon	10.1109/JIOT.2017.2760322	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Internet of Mission-Critical Things: Human and Animal Classification-A Device-Free Sensing Approach	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.for	0805 Distributed Computing	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 - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

© 2014 IEEE. The well-known Internet of Things (IoT) is recently being considered for critical missions, such as search and rescue, surveillance, and border patrol. One of the most critical issues that these applications are currently facing is how to correctly distinguish between human and animal targets in a cost-effective way. In this paper, we present a relatively low-cost, but robust approach that uses a combination of device-free sensing (DFS) and machine-learning technologies to tackle this issue. In order to validate the feasibility of the presented approach, a variety of data is collected in a cornfield using impulse-radio ultra-wideband (IR-UWB) transceivers. These data are then used to investigate the influence of different statistical properties of the radio-frequency (RF) signal on the accuracy of human/animal target classification. Based on the probability density function of different statistical properties, two distinguishing features for target classification are found, namely, standard deviation and root mean spread delay spread. Using them, the impact on the classification accuracy due to different classifiers, number of training samples, and different values of signal-To-noise ratio is extensively verified. Even with the worst case, the classification accuracy of the system is still better than 91% in terms of distinguishing between human and animal targets (including goats and dogs), which indicates that the presented approach has a great potential to be deployed in the near future.

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http://hdl.handle.net/10453/123991