A cognitive WSN framework for highway safety based on weighted cognitive maps and Q-learning
Bibliographic record
Abstract
Wireless technology can provide new efficient techniques for improving highway safety. Wireless Sensor Networks (WSN) have been identified as a key enabling technology for monitoring road conditions and providing early warning messages to drivers about any dangers that may be present. In order to ensure that the end-to-end goals of such a WSN are achieved, this paper proposes a cognitive framework based on the mathematical tools known as Weighted Cognitive Maps (WCM) and Q-learning. WCM is used to design a reasoning machine that can consider multiple conflicting constraints with low complexity. On the other hand, the Q-learning algorithm is used to design a learning protocol that can build a knowledge base which enables the WCM system to make more informed decisions. Thus, a reward system is developed that directly addresses the end-to-end goals of the system. The performance of the cognitive framework is evaluated using extensive computer simulations and compared to state of the art systems. Simulation results show significant performance improvements with the proposed cognitive framework.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".