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    This study introduces a novel sequential learning framework enabling sensors to train each other, even with noisy data. It establishes conditions for sensor instructors and proposes a method to accurately model sensor measurements.

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    Area of Science:

    • Sensor networks
    • Machine learning
    • Statistical signal processing

    Background:

    • Distributed sensor networks require efficient data processing and learning.
    • Training new sensors in a network often relies on external or centralized supervision.
    • Existing methods face challenges with noisy labels and decentralized training.

    Purpose of the Study:

    • To present a sequential learning framework where sensors can train other sensors within a network.
    • To investigate the feasibility and conditions under which sensors can act as instructors.
    • To develop a method for accurate measurement modeling despite potentially noisy training labels.

    Main Methods:

    • A sequential learning framework is proposed, utilizing a few sensors as instructors.
    • Instructors provide estimated labels for new sensor measurements, which may be imperfect.
    • A recursive density estimator, specifically nonparametric kernel density estimation, is employed to refine the measurement model.
    • Convergence rate analysis for the expected error in the observation density is provided.

    Main Results:

    • The study demonstrates that sensors can indeed train other sensors in a network.
    • Necessary conditions for sensors to effectively act as instructors are identified.
    • The proposed recursive density estimator successfully obtains the true measurement model even with noisy labels.
    • Simulation results validate the framework's effectiveness and theoretical findings.

    Conclusions:

    • A viable framework for decentralized sensor training is established, enhancing network autonomy.
    • The recursive density estimation method robustly handles noisy labels, crucial for practical sensor networks.
    • The research provides theoretical guarantees on learning accuracy and convergence within the proposed framework.