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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Grouped-Round-Robin-based state estimation for multiplex networks subject to deception attacks.

Zewei Yang¹, Luyang Yu², Dan Liu²

¹School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China.

ISA Transactions

|December 31, 2025

Summary

Researchers developed a new Grouped-Round-Robin (GRR) protocol for state estimation in nonlinear time-delayed multiplex networks (NTD-MNs). This method enhances network observability and improves estimator performance against deception attacks.

Keywords:

Deception attacks Grouped-Round-Robin protocol Multiplex networks State estimation

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Area of Science:

Control Systems Engineering
Network Security
Cyber-Physical Systems

Background:

Nonlinear time-delayed multiplex networks (NTD-MNs) face state estimation challenges.
Classical Round-Robin (RR) protocols can degrade estimator performance due to reduced observability.
Cyber-attacks, specifically deception attacks, compromise network security and data integrity.

Purpose of the Study:

To propose a novel Grouped-Round-Robin (GRR) protocol for improved state estimation in NTD-MNs.
To address the performance degradation caused by conventional RR protocols.
To account for stochastic deception attacks in the state estimation framework.

Main Methods:

Introduced a Grouped-Round-Robin (GRR) scheduling mechanism for data transmission.
Developed a state estimator considering deception attacks on measurement outputs.
Derived sufficient conditions for ensuring the estimation error is ultimately bounded in mean square.

Main Results:

The proposed GRR protocol enhances network observability compared to the RR protocol.
The developed state estimator effectively handles stochastic deception attacks.
Sufficient conditions for bounded estimation error were theoretically established.

Conclusions:

The GRR protocol offers a superior approach for state estimation in NTD-MNs under deception attacks.
The study provides a robust framework for secure and reliable state estimation in complex networks.
Numerical simulations validated the theoretical findings and the effectiveness of the proposed method.