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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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/14/3112 |
| _version_ | 1818040945671667712 |
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| author | Raquel Caballero-Águila Aurora Hermoso-Carazo Josefa Linares-Pérez |
| author_facet | Raquel Caballero-Águila Aurora Hermoso-Carazo Josefa Linares-Pérez |
| author_sort | Raquel Caballero-Águila |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T08:22:36Z |
| format | Article |
| id | doaj.art-a47e90f825694491984b47d609a8473a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T08:22:36Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a47e90f825694491984b47d609a8473a2022-12-22T01:56:19ZengMDPI AGSensors1424-82202019-07-011914311210.3390/s19143112s19143112Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception AttacksRaquel Caballero-Águila0Aurora Hermoso-Carazo1Josefa Linares-Pérez2Dpto. de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, SpainDpto. de Estadística, Universidad de Granada, Avda. Fuentenueva, 18071 Granada, SpainDpto. de Estadística, Universidad de Granada, Avda. Fuentenueva, 18071 Granada, SpainIn 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.https://www.mdpi.com/1424-8220/19/14/3112least-squares filteringleast-squares fixed-point smoothingnetworked systemscluster-based approachstochastic deception attacks |
| spellingShingle | Raquel Caballero-Águila Aurora Hermoso-Carazo Josefa Linares-Pérez Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks Sensors least-squares filtering least-squares fixed-point smoothing networked systems cluster-based approach stochastic deception attacks |
| title | Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks |
| title_full | Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks |
| title_fullStr | Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks |
| title_full_unstemmed | Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks |
| title_short | Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks |
| title_sort | covariance based estimation for clustered sensor networks subject to random deception attacks |
| topic | least-squares filtering least-squares fixed-point smoothing networked systems cluster-based approach stochastic deception attacks |
| url | https://www.mdpi.com/1424-8220/19/14/3112 |
| work_keys_str_mv | AT raquelcaballeroaguila covariancebasedestimationforclusteredsensornetworkssubjecttorandomdeceptionattacks AT aurorahermosocarazo covariancebasedestimationforclusteredsensornetworkssubjecttorandomdeceptionattacks AT josefalinaresperez covariancebasedestimationforclusteredsensornetworkssubjecttorandomdeceptionattacks |