Dynamic <i>k</i> -coverage planning for multiple events with mobile robots
Bibliographic record
Abstract
Dynamic k-coverage planning for multiple events with mobile robots is proposed in the article. In mobile sensor networks, movement with the minimum energy for multiple events detection is a challenge which is discussed in the article. The problem of multiple events coverage is divided into two subproblems, namely mobile robots’ uniform deployment and nodes’ selection. Assuming that sparse mobile robots randomly deploy in the environment, mobile robots need to uniformly deploy firstly in order to effectively communicate with static nodes and extremely cover the entire region. A weighted-sub-Voronoi-half-gravity method and a weighted-sub-Voronoi-half-incenter method are presented for mobile robots’ uniform deployment. Two algorithms guarantee mobile robots are deploying with a higher coverage ratio. Meanwhile, analog game theoretic algorithm is proposed for nodes’ selection (static node’s selection and mobile robots’ selection). Only one static node is selected to detect an event and notifies candidate mobile robots which can communicate with the selected one of the event’s occurrence. Moreover, k mobile robots are selected for event coverage. The proposed algorithm achieves k-coverage of each event with less energy consumption. Performance analysis and simulations show that the proposed algorithm achieves very good results.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".