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

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 network...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 19; H. 14; S. 3112
Hauptverfasser: Caballero-Águila, Raquel, Hermoso-Carazo, Aurora, Linares-Pérez, Josefa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 14.07.2019
MDPI
Schlagworte:
Algorithms
Big Data
cluster-based approach
Clustering
Deception
Design
False information
least-squares filtering
least-squares fixed-point smoothing
networked systems
Sensors
stochastic deception attacks
Wireless networks
Spain
least-squares filtering
least-squares fixed-point smoothing
cluster-based approach
networked systems
stochastic deception attacks
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19143112