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Optimal jamming power allocation against remote state estimation
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2017 (English)In: American Control Conference (ACC), 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1660-1665, article id 7963191Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates a remote state estimation problem, where a smart sensor observes the state of a physical plant, locally estimates the state with Kalman filter, and then sends its local estimation data to a remote estimator through a communication network. There is a jamming attacker in the communication network who intentionally blocks the network with the purpose of deteriorating estimation quality subject to the jamming energy constraint. From the viewpoint of jamming attacker, in order to maximize attack effect, we focus on the questions 'when to jam the communication network', and 'how much power the attacker should adopt to jam the network'. After theoretic analysis, we point out that any grouped together attack schedule is optimal for the given jamming power. We provide a close form of jamming power when a sufficient condition holds, and the corresponding jamming effect on remote state estimation is explicitly presented. For a general case, we design a jamming power allocation algorithm and show the computational complexity of the proposed algorithm is not worse than O(T), where T is the length of the time horizon considered.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 1660-1665, article id 7963191
Series
Proceedings of the American Control Conference, ISSN 0743-1619
Keywords [en]
State estimation, Energy constraint, Estimation quality, Jamming power allocations, Local estimation, Physical plants, Remote state estimations, Theoretic analysis, Time horizons, Jamming
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-212469DOI: 10.23919/ACC.2017.7963191ISI: 000427033301115Scopus ID: 2-s2.0-85026855488ISBN: 9781509059928 (print)OAI: oai:DiVA.org:kth-212469DiVA, id: diva2:1135335
Conference
2017 American Control Conference, ACC 2017, Sheraton Seattle Hotel, Seattle, United States, 24 May 2017 through 26 May 2017
Note

QC 20170823

Available from: 2017-08-23 Created: 2017-08-23 Last updated: 2018-04-10Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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