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Distributed Robust Filtering for Wireless Sensor Networks with Markov Switching Topologies and Deception Attacks.

Fengzeng Zhu¹, Xu Liu¹, Jiwei Wen¹

¹Engineering Research Center of Internet of Things Applied Technology, Jiangnan University, Ministry of Education, Wuxi 214122, Jiangsu, China.

Sensors (Basel, Switzerland)

|April 5, 2020

Summary

This summary is machine-generated.

This study addresses state estimation for systems with switching networks and deception attacks. A new filter design ensures stability and performance despite random communication changes and security threats.

Keywords:

deception attack distributed filtering linear matrix inequality switching topology wireless sensor network

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Area of Science:

Control Systems Engineering
Network Security
Stochastic Systems

Background:

Distributed state estimation is crucial for wireless sensor networks (WSNs).
Systems face challenges from randomly switching topologies and sophisticated deception attacks.
Ensuring robust filtering performance under these conditions is a significant research gap.

Purpose of the Study:

To develop distributed full- and reduced-order state estimators for discrete time-invariant systems.
To account for randomly occurring switching topologies modeled by a homogeneous Markov chain.
To address sector-bound deception attacks characterized by Bernoulli-distributed random variables.

Main Methods:

A novel switching topology model incorporating a homogeneous Markov chain.
Inclusion of sector-bound deception attacks with Bernoulli distribution for random occurrence.
Design of adjustable parameter E to achieve full- and reduced-order estimators.
Establishment of sufficient conditions via convex optimization for filter solvability.

Main Results:

A distributed state estimator design is proposed for systems with switching topologies and deception attacks.
The filter guarantees exponential mean-square stability and a prescribed l2-l∞ performance index.
The approach effectively handles stochastic external interference and network uncertainties.

Conclusions:

The developed method provides an effective and flexible solution for robust state estimation in WSNs.
The convex optimization framework ensures the existence of the designed distributed filters.
Simulation results validate the proposed approach's performance and applicability.