An energy efficient hybrid interference-resilient frame fragmentation for wireless sensor networks
Bibliographic record
Abstract
Frame fragmentation into small blocks with dedicated error detection codes per block can reduce the unnecessary retransmission of the correctly received blocks. However, the optimal block size varies based on the wireless channel conditions. Further, blocks within a single frame may have different optimal sizes based on variations in interference patterns. This paper proposes a hybrid interference-resilient frame fragmentation (Hi-Frag) link-layer scheme for wireless sensor networks. It effectively addresses the challenges associated with dynamic partitioning of blocks while accounting for the observed error patterns. Hi-Frag is the first work to introduce an adaptive frame fragmentation scheme with hybrid block sizing, implemented and evaluated on a real WSN testbed. Hi-Frag shows substantial enhancements over fixed-size partial packet recovery protocols, achieving up to 2.5× improvement in throughput when the channel condition is noisy, while reducing network delays by up to 14% of the observed delay. On average, Hi-Frag shows 35% gain in throughput compared to static fragmentation approaches across all channel conditions used in our experiments. Also, Hi-Frag lowers the energy consumed per useful bit by 66% on average compared to conventional protocols, which increases the energy efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".