An Observer-Based Key Agreement Scheme for Remotely Controlled Mobile Robots
Bibliographic record
Abstract
Remotely controlled mobile robots are important examples of Cyber-Physical Systems (CPSs). Recently, these robots are being deployed in many safety critical applications. Therefore, ensuring their cyber-security is of paramount importance. Different control schemes that have been proposed to secure such systems against sophisticated cyber-attacks require the exchange of secret messages between their smart actuators and the remote controller. Thus, these schemes require pre-shared secret keys, or an established Public Key Infrastructure (PKI) that allows for key agreement. Such cryptographic approaches might not always be suitable for the deployment environments of such remotely mobile robots. To address this problem, in this paper, we consider a control theoretic approach for establishing a secret key between the remotely controlled robot and the networked controller without resorting to traditional cryptographic techniques. Our key agreement scheme leverages a nonlinear unknown input observer and an error correction code mechanism to allow the robot to securely agree on a secret key with its remote controller. To validate the proposed scheme, we implement it using a Khepera-IV differential drive robot and evaluate its efficiency and the additional control cost acquired by it. Our experimental results confirm the effectiveness of the proposed key establishment scheme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".