Randomized Robot-Assisted Relocation of Sensors for Coverage Repair in Wireless Sensor Networks
Bibliographic record
Abstract
In wireless sensor networks (WSN), stochastic node dropping and unpredictable node failure greatly impair coverage, creating sensing holes, while locally redundant sensors exist. If sensors are all equipped with locomotion, they will be able to relocate themselves to improve coverage. But this approach increases the complexity of hardware design for sensors as well as deployment budget. In this paper, we consider a small group of mobile robots to serve WSN. We propose an algorithm, named Randomized Robot-assisted Relocation of Static Sensors (R3S2), for coverage repair and a grid-based variant, called G-R3S2. By these algorithms, mobile robots move within the network to collect redundant sensors and deliver them to reported sensing hole positions. In R3S2, robots move completely at random and relocate encountered redundant sensors. In G-R3S2, the robots random movement is restricted on a virtual grid, and the robots continually move to the next least recently visited grid point so as to increase the chance of discovering redundant sensors and sensing holes. Through extensive simulation, we show their effectiveness and practicality and evaluate their performance. The simulation results indicate in particular that G-R3S2 outperforms R3S2 across all measured metrics.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".