Scheduled rendezvous and RFID wakeup in embedded wireless networks
Bibliographic record
Abstract
Scheduled rendezvous is a common technique for reducing power consumption in embedded wireless networks. In scheduled rendezvous, nodes remain in a low power sleep mode whenever possible and periodically awaken to rendezvous with other nodes. Unfortunately, in many embedded wireless systems node power consumption may be unnecessarily dominated by this rendezvous activity. We study the use of radio frequency identification (RFED) technology, as a low power wakeup mechanism for embedded radio networks. RFED radios are very low cost and can currently be operated at power consumptions of over three orders of magnitude lower than that of typical commercial radios operating in the Mbps range. We first compare the regions of operation where RFED wakeup and scheduled rendezvous are preferred. A protocol is proposed which allows the basestation to block transmissions that may interfere with the wakeup process. In addition, a hybrid low power rendezvous wakeup protocol is proposed which attains very low power consumption. We find that in low utilization situations where a high level of responsiveness is needed, low power wakeup can achieve much lower levels of power consumption than scheduled rendezvous. The results also suggest that adaptive schemes are possible where the mode used is selected dynamically by the basestation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".