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Distributed H∞ Estimation in Sensor Networks With Two-Channel Stochastic Attacks.

Haiyu Song, Peng Shi, Wen-An Zhang

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    This study introduces a new distributed estimation model for sensor networks facing unknown communication attacks. The model ensures reliable data estimation despite stochastic attacks affecting sensor and inter-sensor channels.

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    Area of Science:

    • Control Systems Engineering
    • Network Security
    • Signal Processing

    Background:

    • Sensor networks are vulnerable to unknown attacks on communication channels.
    • Attacks can disrupt data transmission between plants, sensors, and among sensors.
    • Stochastic attack models are crucial for realistic network security analysis.

    Purpose of the Study:

    • To develop a novel distributed estimation model for sensor networks under two-channel stochastic attacks.
    • To ensure robust and reliable state estimation in the presence of unknown network intrusions.
    • To design effective distributed H∞ estimators for enhanced network security.

    Main Methods:

    • A new distributed estimation model is proposed, integrating sensor measurements and neighbor information.
    • The study derives a sufficient condition for the existence of distributed H∞ estimators.
    • Distributed estimator gains are designed by solving linear matrix inequalities (LMIs).

    Main Results:

    • A novel distributed estimation framework is presented to counter two-channel stochastic attacks.
    • A condition for the existence of the proposed distributed H∞ estimators is established.
    • The effectiveness of the design techniques is validated through two illustrative examples.

    Conclusions:

    • The proposed distributed estimation approach effectively addresses stochastic attacks in sensor networks.
    • The developed H∞ estimators provide a robust solution for secure distributed estimation.
    • The LMI-based design offers a practical method for implementing secure sensor networks.