Energy Efficient Scheduling Algorithms for Sweep Coverage in Mobile Sensor Networks
Bibliographic record
Abstract
Nowadays, with the development of micro-electro-mechanical technologies, sweep coverage with mobile sensors is more and more popular in wireless sensor networks, which is also applied widely in other scenarios, such as message ferrying and data routing in ad-hoc networks. In order to reduce the sweep cycle and the number of mobile sensors, we propose the Distance-Sensitive-Route-Scheduling (DSRS) problem, which is to consider the effect of sensing range. We prove that DSRS is NP-hard, and consider three different scenarios: the single sensing-point case, the general case, and the extended case. In the single sensing-point case, we propose an approximation algorithm ROSE to schedule the routes of the mobile sensors efficiently. For the general case and the extended case, we present two other approximation algorithms G-ROSE and E-ROSE based on ROSE. We further characterize the non-locality property and design a distributed algorithm D-ROSE, coordinating sensors to meet the sweep requirements with best effort. Our algorithms are scalable to different sweep coverage problems, and according to the simulation results, they greatly outperform other existing algorithms up to 45 percent especially with a large sensing range.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".