Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks

María Jesús García-Ligero, Aurora Hermoso-Carazo and Josefa Linares-Pérez
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María Jesús García-Ligero: Departamento de Estadística e I. O., Universidad de Granada, Avda Fuentenueva s/n, 18071 Granada, Spain
Aurora Hermoso-Carazo: Departamento de Estadística e I. O., Universidad de Granada, Avda Fuentenueva s/n, 18071 Granada, Spain
Josefa Linares-Pérez: Departamento de Estadística e I. O., Universidad de Granada, Avda Fuentenueva s/n, 18071 Granada, Spain

Mathematics, 2022, vol. 10, issue 4, 1-17

Abstract: This paper focuses on the distributed fusion estimation problem in which a signal transmitted over wireless sensor networks is subject to deception attacks and random delays. We assume that each sensor can suffer attacks that may corrupt and/or modify the output measurements. In addition, communication failures between sensors and their local processors can delay the receipt of processed measurements. The randomness of attacks and transmission delays is modelled by different Bernoulli random variables with known probabilities of success. According to these characteristics of the sensor networks and assuming that the measurement noises are cross-correlated at the same time step between sensors and are also correlated with the signal at the same and subsequent time steps, we derive a fusion estimation algorithm, including prediction and filtering, using the distributed fusion method. First, for each sensor, the local least-squares linear prediction and filtering algorithm are derived, using a covariance-based approach. Then, the distributed fusion predictor and the corresponding filter are obtained as the matrix-weighted linear combination of corresponding local estimators, checking that the mean squared error is minimised. A simulation example is then given to illustrate the effectiveness of the proposed algorithms.

Keywords: distributed fusion estimation; sensor networks; deception attacks; random delays; correlated noises (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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