Joint Anti-Interference and Anti-Collision for ABS-Assisted Medical-Care Sensor Networks
Bibliographic record
Abstract
Medical-care sensor networks promote the rapid development of telemedicine applications. However, in poverty-struck, disaster-struck or remote areas with limited infrastructures, it is difficult to provide fast and timely medical-care services. To address this challenge, we propose an aerial base station (ABS)-assisted medical-care sensor network, based on which the data transmission problem is investigated by jointly considering the anti-interference and anti-collision requirements. Specifically, in order to reduce the bit error rate caused by electromagnetic interferences, we first design an anti-interference method based on M-ary spread spectrum and multi-carrier modulation. Then, by introducing a multi-frequency sensor identification mechanism, an anti-collision method based on time division multiple access and frequency division multiple access is presented. Finally, simulation results demonstrate that our proposed scheme has significant advantages in anti-collision and anti-interference compared with current schemes. In quad-interference scenarios, the anti-interference performance is improved by 5.3 dB. Moreover, the anti-collision performance is also increased by 17.2%. Furthermore, in scenarios with a large number of sensors, the successful sensor identification percentage is always greater than 50%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 teacher head, 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".