Energy-Efficient Semi-Flocking Control of Mobile Sensor Networks on Rough Terrains
Bibliographic record
Abstract
Mobile sensor networks (MSNs) with semi-flocking control protocols have demonstrated promising performances in both area coverage and target tracking. However, they may not operate at their highest efficiencies due to poor utilization of local information and deficient motion coordinations among mobile nodes. In this brief, a distributed semi-flocking control protocol based on local information exchanges is proposed to address the above issues in MSNs. Most existing semi-flocking control protocols are designed for patrolling in flat terrains and maneuvering nodes using shortest paths between two points on the given terrains. Such assumptions and the corresponding decisions do not apply well on real-world rough terrains and they often impose extra energy expenditure to mobile nodes. To address this problem, a terrain adaptation force and a navigation goal selection method are integrated into the proposed control protocol. Our study on rough terrains illustrates that the proposed control protocol is capable of achieving better performances in both area coverage and target tracking with lower energy expenditure when compared to the state-of-the-art flocking-based control protocols.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".