A Secure Decentralized Event-Triggered Cooperative Localization in Multi-Robot Systems Under Cyber Attack
Bibliographic record
Abstract
We study the problem of secure decentralized event-triggered cooperative localization (SDECL) for a team of mobile robots in an adversarial environment, where the objective is to perform localization in the presence of a malicious attacker. We consider a scenario in which an intruder is able to attack the communication channels between exteroceptive sensors and filter of the robot and between two robots independently. First, we design a secure decentralized event-triggered cooperative localization in the multi-robot system against random Denial of Service (DoS) and False Data Injection (FDI) attacks. Then, we provide sufficient conditions that ensure the resilience and convergence of the proposed algorithm when the attacker signal rate is bounded. Simulation results show that by properly tuning the parameters of the event-triggered mechanism and considering bounded attack rate, the proposed algorithm is resilient against cyber attacks. Also, experimental results using four e-puck2 mobile robots have demonstrated the effectiveness of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".