Source identification under sensor observations: A Gaussian source estimator

Jiang, J; Wen, S; Yu, S; Liu, B; Xiang, Y; Zhou, W

Source identification under sensor observations: A Gaussian source estimator

Jiang, J Wen, S Yu, S

Liu, B

Xiang, Y Zhou, W

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Publication Type:: Chapter
Citation:: Advances in Information Security, 2019, 73 pp. 89 - 93
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Jiang, J	en_US
dc.contributor.author	Wen, S	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617	en_US
dc.contributor.author	Xiang, Y	en_US
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Advances in Information Security, 2019, 73 pp. 89 - 93	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137315
dc.description.abstract	© Springer Nature Switzerland AG 2019. In this chapter, we introduce a propagation source estimator under sensor observations: Gaussian source estimator. According to Chap. 5, sensors are firstly injected into networks, and then the propagation dynamics over these sensor nodes are collected, including their states, state transition time and infection directions. There have been many approaches proposed under sensor observations, including Bayesian based method, Gaussian based method, Moon-Walk based method, etc. Here, we particular present the details of the Bayesian based method. For the techniques involved in other methods, readers could refer to Chap. 9 for details.	en_US
dc.relation.ispartof	Advances in Information Security	en_US
dc.relation.isbasedon	10.1007/978-3-030-02179-5_8	en_US
dc.title	Source identification under sensor observations: A Gaussian source estimator	en_US
dc.type	Chapter
utslib.citation.volume	73	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	73	en_US

Abstract:

© Springer Nature Switzerland AG 2019. In this chapter, we introduce a propagation source estimator under sensor observations: Gaussian source estimator. According to Chap. 5, sensors are firstly injected into networks, and then the propagation dynamics over these sensor nodes are collected, including their states, state transition time and infection directions. There have been many approaches proposed under sensor observations, including Bayesian based method, Gaussian based method, Moon-Walk based method, etc. Here, we particular present the details of the Bayesian based method. For the techniques involved in other methods, readers could refer to Chap. 9 for details.

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http://hdl.handle.net/10453/137315