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Updated: May 23, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Adaptive self-triggered distributed filtering over sensor networks with partially unknown probabilities.

Zhongqi Li1, Fengzeng Zhu1, Ancai Zhang1

  • 1School of Automation and Electrical Engineering, Linyi University, Linyi, 276005, China.

ISA Transactions
|March 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive self-triggered strategy (ASTS) for distributed estimation, reducing energy use in networks with unknown communication patterns. The method ensures reliable estimation through advanced filter design and validation.

Keywords:
Adaptive self-triggered strategyMarkov switching topologiesPartial probability unknown

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Area of Science:

  • Control Engineering
  • Networked Systems
  • Signal Processing

Background:

  • Distributed estimation systems often face challenges with energy consumption due to continuous communication.
  • Network topology dynamics, especially with partially unknown transition probabilities, complicate filter design.
  • Minimizing communication overhead is crucial for the efficiency of large-scale networked systems.

Purpose of the Study:

  • To develop a novel distributed estimation approach utilizing a topology-switching structure.
  • To introduce an adaptive self-triggered strategy (ASTS) for minimizing inter-node communication energy consumption.
  • To design and validate a robust filter capable of handling time-varying network topologies with probabilistic switching.

Main Methods:

  • Modeling the network communication topology as a time-varying process governed by a homogeneous Markov chain.
  • Employing an adaptive self-triggered strategy (ASTS) to optimize communication events.
  • Utilizing Lyapunov stability theory and linear matrix inequality (LMI) methods for filter parameter determination.
  • Verifying filter design feasibility and performance.

Main Results:

  • The proposed adaptive self-triggered strategy effectively reduces energy consumption in distributed estimation.
  • The filter design accommodates network topology switching governed by a probabilistic transition matrix with unknown data.
  • Lyapunov stability theory and LMI methods successfully determined filter parameters ensuring feasibility.
  • Numerical simulations and experimental validation confirmed the approach's effectiveness.

Conclusions:

  • The developed distributed estimation approach with ASTS offers significant energy savings for networked systems.
  • The methodology provides a robust framework for handling dynamic and partially unknown network topologies.
  • The approach is validated for practical applications, demonstrated through a continuous stirred tank reactor experiment.