Energy-Efficient Semi-Flocking Control of Mobile Sensor Networks on Rough Terrains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it