MétaCan
Menu
Back to cohort
Record W4411171829 · doi:10.1109/tsmc.2025.3571020

Event-Triggered Fault-Tolerant Consensus Control of Multiagent Systems With Hybrid Attacks

2025· article· en· W4411171829 on OpenAlexaff
Chun Liu, Bin Jiang, Youmin Zhang, Xiaoqiang Ren, Xiaofan Wang

Bibliographic record

VenueIEEE Transactions on Systems Man and Cybernetics Systems · 2025
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsConcordia University
FundersScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsComputer scienceMulti-agent systemConsensusFault toleranceDistributed computingEvent (particle physics)Artificial intelligence

Abstract

fetched live from OpenAlex

In this study, the fault-tolerant consensus control (FTCC) challenge is investigated for nonlinear multiagent systems (MASs) in the simultaneous occurrence of abrupt and incipient actuator/sensor faults in the physical level and hybrid Deception/Denial-of-Service (DoS) attacks in the cyber level. For security enhancement and/or safety maintenance purposes, an unknown state and fault decoupling-based augmented estimator is first devised, and a distributed event-triggered FTCC protocol is then developed to achieve strength against hostile attacks and faults, respectively, with the incorporation of augmented state estimation, neighboring sensor fault estimation, and latest successfully triggered output interaction. By constructing dual indicators along with average dwelling time and attack frequency technique, criteria of exponential mean-square consensus of the nonlinear MASs subject to hybrid attacks are obtained. In the end, simulation is outlined to illustrate the efficacy and improvements of the developed event-triggered FTCC methodology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.227
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Systems Man and Cybernetics SystemsSame topicDistributed systems and fault toleranceFrench-language works237,207