Employing IEEE 802.15.4 for Quality of Service Provisioning in Wireless Body Area Sensor Networks
Bibliographic record
Abstract
A wireless body area sensor network (WBASN) is a wireless networking technology that interconnects tiny nodes with sensing capabilities in, on or around a human body. The vital-sign information collected by these sensors can then be used by care-givers to assess the health of a patient. Therefore, it is necessary to provide reliable communication services to prioritized WBASN data streams with quality of service (QoS) guarantees. However, existing research has not devoted enough attention to this issue. This paper proposes a QoS provisioning framework for WBASN traffic employing the IEEE 802.15.4 super-frame structure in the beacon-enabled mode. A method of prioritizing WBASN traffic is proposed, along with algorithms for admission control and scheduling. Computer simulations reveal that our scheme is able to guarantee a 100% time constraint compliance ratio for traffic in contention access periods, and a 99.2% to 99.9% constraint compliance ratio for traffic in contention-free periods, while still admitting and accommodating tens of WBASN traffic streams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".