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Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks

1
Dpto. de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain
2
Dpto. de Estadística, Universidad de Granada, Avda. Fuentenueva, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(14), 3112; https://doi.org/10.3390/s19143112
Received: 30 May 2019 / Revised: 27 June 2019 / Accepted: 12 July 2019 / Published: 14 July 2019
(This article belongs to the Section Sensor Networks)
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Abstract

In this paper, a cluster-based approach is used to address the distributed fusion estimation problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of random deception attacks. At each sampling time, measured outputs of the signal are provided by a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local processor which gathers the measured outputs of its sensors and, in turn, the local processors of all clusters are connected with a global fusion center. The proposed cluster-based fusion estimation structure involves two stages. First, every single sensor in a cluster transmits its observations to the corresponding local processor, where least-squares local estimators are designed by an innovation approach. During this transmission, deception attacks to the sensor measurements may be randomly launched by an adversary, with known probabilities of success that may be different at each sensor. In the second stage, the local estimators are sent to the fusion center, where they are combined to generate the proposed fusion estimators. The covariance-based design of the distributed fusion filtering and fixed-point smoothing algorithms does not require full knowledge of the signal evolution model, but only the first and second order moments of the processes involved in the observation model. Simulations are provided to illustrate the theoretical results and analyze the effect of the attack success probability on the estimation performance. View Full-Text
Keywords: least-squares filtering; least-squares fixed-point smoothing; networked systems; cluster-based approach; stochastic deception attacks least-squares filtering; least-squares fixed-point smoothing; networked systems; cluster-based approach; stochastic deception attacks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J. Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks. Sensors 2019, 19, 3112.

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