Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks
Metadata
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MDPI
Materia
Least-squares filtering Least-squares fixed-point smoothing Networked systems Cluster-based approach Stochastic deception attacks
Date
2019-07-14Referencia bibliográfica
Caballero-Águila, R., Hermoso-Carazo, A., & Linares-Pérez, J. (2019). Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks. Sensors, 19(14), 3112.
Sponsorship
This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P).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.