End-to-End Acknowledgement for Data Collection in Wireless Sensor Networks
Bibliographic record
Abstract
A novel method to improve the reliability of data collection in wireless sensor networks is presented. The Disseminated ACKnowledgment protocol (DACK) builds on collection and dissemination protocols to provide end-to-end acknowledgement of data samples. A DACK protocol implementation was tested using simulations and experiments on TelosB motes running TinyOS 2.1. Experiments were carried out on three floors of a building, with 14 motes transmitting data samples continuously until battery exhaustion. Results show that the DACK protocol recovers all data samples that would have been lost using a collection protocol only. The benefit of increased data collection reliability comes at the cost of increased communication. In one experiment with 14 motes, 719 data samples were dropped from a total of 749,904 data samples sent over six days; all these dropped samples were recovered using the DACK protocol. This experiment required an additional 720 collection packets to resend the dropped samples (in addition to the original 467,778 collection packets) plus 18,733 DACK packets. The extra energy required to send these DACK packets was determined experimentally to be negligible. The DACK packet to original collection packet ratio of 0.04 in the experiment was observed to increase to 0.078 in a simulation of 16 motes in a much noisier 4 by 4 grid 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".