On the role of multi-objective optimization in risk mitigation for critical infrastructures with robotic sensor networks
Bibliographic record
Abstract
The use of robotic sensor networks (RSNs) for Territorial Security (TerrSec) applications has earned an increasing popularity in recent years. In Critical Infrastructure Protection (CIP) applications, the RSN goal is to provide the information needed to maintain a secure perimeter around the desired infrastructure and efficiently coordinate a corporate response to any event that arises in the monitored region. Such a response will only involve the most suitable robotic nodes and must successfully counter any detected vulnerability in the system. This paper is a preliminary study of the role played by multi-objective optimization (MOO) in the elicitation of responses from a risk-aware RSN that is deployed around a critical infrastructure. Contrary to previous studies showcasing a pre-optimization auctioning scheme, where the RSN nodes bid on the basis of their knowledge of the event, we introduce a post-optimization auctioning scheme in which the nodes place their bids knowing what their final positions along the perimeter will be, hence calling for a more informed decision at the node level. The impact of the pre- vs. post-optimization stage in a first-price sealed bid auction system over the risk mitigation strategies elicited by the RSN is evaluated and discussed. Empirical results reveal that the pre-optimization auctioning is more suitable for dense RSNs whereas the post-optimization one is preferred in sparse RSNs. To the best of our knowledge, this is the first attempt to assess the role of MOO in risk mitigation for CIP with RSNs.
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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".