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Updated: Jan 13, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Event-Triggered Practical Finite-Time Distributed Optimization for Networked Multiagent Systems With Edge-Based

Jiahao Leng, Qishui Zhong, Lanfeng Hua

    IEEE Transactions on Cybernetics
    |January 6, 2026
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    Summary
    This summary is machine-generated.

    This study introduces a robust distributed optimization algorithm for networked multiagent systems facing measurement noise. The novel approach ensures faster, finite-time convergence for optimal solutions, enhancing system efficiency.

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    Last Updated: Jan 13, 2026

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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    Area of Science:

    • Control Systems Engineering
    • Networked Multiagent Systems
    • Optimization Theory

    Background:

    • Distributed optimization problems (DOPs) are crucial for networked multiagent systems (NMASs).
    • Existing methods often struggle with time-varying parameters and measurement noise.
    • Robustness and efficiency in NMASs remain key challenges.

    Purpose of the Study:

    • To develop a robust distributed optimization algorithm (DOA) for time-varying NMASs over directed graphs.
    • To address the impact of edge-based additive measurement noise (EBAMN).
    • To improve convergence rates and communication efficiency.

    Main Methods:

    • Establishment of a finite-time stochastic stability framework.
    • Development of a novel continuous-time DOA with consensus-gain function, state-dependent gains, and integral gradient information.
    • Application of Itô lemma and Lyapunov theory for convergence analysis.
    • Integration of an adaptive dynamic event-triggered mechanism (ETM).

    Main Results:

    • Global stochastic practical finite-time attraction of the origin demonstrated.
    • Guaranteed $p$th moment convergence and practical finite-time consensus in probability.
    • Convergence of NMAS states to the time-varying optimal solution despite EBAMN.
    • Significant enhancement in communication efficiency and resource reduction via the adaptive ETM, preventing Zeno behavior.

    Conclusions:

    • The proposed robust continuous-time DOA effectively handles time-varying DOPs in NMASs with EBAMN.
    • The adaptive ETM significantly improves communication efficiency and resource management.
    • Numerical simulations using multi-UAV target tracking validate the algorithm's robustness and performance.