Reprogramming over Low Power Link Layer in Wireless Sensor Networks
Bibliographic record
Abstract
Reprogramming over the air is important for maintaining a wireless sensor network. Traditional reprogramming approaches assume always-on link layers. Given the energy limitation of sensor nodes, always-on link layers are often not desired for most sensor network applications. In this paper, we propose ROLP, a novel reprogramming protocol built on a widely used low power link layer in wireless sensor networks. ROLP employs an efficient control packets self-suppression scheme for reliable data transmission. ROLP also employs an adaptively falling asleep scheme based on the neighbor information to reduce the energy consumption. We implement ROLP based on TinyOS and evaluate its performance in two different indoor networks. Compared with the standard reprogramming protocol in TinyOS, ROLP is able to reduce the radio-on time by 57.6% and 39.0% in the two networks. Since radio operations cost most of the energy, these reductions save significant amount of energy and prolong the lifetime of a wireless sensor network.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".