On the Impact of Sweep Radius and Energy Limitation on Sweep Coverage in Wireless Sensor Networks
Bibliographic record
Abstract
Sweep coverage is an important problem in Wireless Sensor Networks (WSNs). Technically, sweep coverage makes use of mobile sensor nodes that move around to collect sensing data from Points of Interest (POIs) at a low cost. Since POIs can often be sensed remotely, mobile sensor nodes do not have to arrive at the location of POIs to gather sensing data. Sweep radius, the maximum distance between a mobile sensor node and a POI that enables sensing, is an important factor in sweep coverage planning. In addition, because mobile sensor nodes are typically powered by batteries, they tend to have a limited lifetime. To continue the coverage, mobile sensor nodes have to periodically return to the base station to replenish their energy. In this paper, sweep coverage based on sweep radius and energy limitation is formulated as the (t, T, R)-SCBR problem. To tackle the (t, T, R)-SCBR problem, a centralized algorithm (i.e. CPS) and a distributed algorithm (i.e. DPP) are proposed. Through extensive simulations, we found that the proposed algorithms significantly outperform the existing schemes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".