No-reboot and zero-flash over-the-air programming for Wireless Sensor Networks
Bibliographic record
Abstract
Over-the-air reprogramming is an important aspect in the deployment and management of Wireless Sensor Networks (WSNs). However, WSNs reprogramming poses significant challenges due to scarce available energy, low computational power, and limited memory capabilities of the WSNs nodes; all are required for transmission and processing of the created patches. In existing reprogramming schemes, any change in the program layout and/or global variables, produces a significantly large patch size, hence consumes the node's limited resources. Furthermore, to apply the patch, existing schemes require rewriting internal flash, large volume of external flash, as well as rebooting the node. In this paper, we devise a novel reprogramming scheme that we call Queen's Differential (QDiff), which mitigates the effects of program layout changes and retains the maximum similarity between ”old” and ”new” codes using clone detection techniques. Moreover, QDiff organizes the global variables in a novel way to eliminate the effect of variable shifting. To assess the performance of Qdiff, we have carried out a TinyOS implementation using an IRIS mote platform. Our experiments show that QDiff requires near-zero external flash, and significantly lower internal flash rewriting and transmission overhead than leading existing differential reprogramming mechanisms.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".