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Cluster-based resilient distributed estimation through adversary detection

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journal contribution
posted on 2020-01-01, 00:00 authored by F Gao, Q Yu, L Bai, J Wang, Jinho ChoiJinho Choi
© The Institution of Engineering and Technology 2019. Security becomes increasingly important due to various attacks from adversaries in wireless sensor networks. This work considers a resilient distributed estimation of an unknown parameter with a cluster-based approach when some agents are adversarial. A two-phase algorithm is adopted to perform parameter estimation and detect attacks. First, a cluster scheme is proposed to make sure that each cluster is connected. Then, the attack is detected and estimation is achieved with a consensus +innovation estimator in each cluster. Finally, the cluster heads combine the consensus estimates in each cluster and exchange with other cluster heads to achieve unknown parameter estimation. In addition, the detection sensitivity under different cluster schemes is also compared. Numerical examples illustrate that the proposed cluster-based approach can improve the convergence rate and detection sensitivity.

History

Journal

IET Communications

Volume

14

Issue

3

Pagination

451 - 457

Publisher

Institution of Engineering and Technology

Location

Stevenage, Eng.

ISSN

1751-8628

Language

eng

Publication classification

C1 Refereed article in a scholarly journal