Performance Analysis of IEEE 802.15.6-Based Coexisting Mobile WBANs With Prioritized Traffic and Dynamic Interference
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Bibliographic record
Abstract
Intelligent wireless body area networks (WBANs) have entered into an incredible explosive popularization stage. WBAN technologies facilitate real-time and reliable health monitoring in e-healthcare and creative applications in other fields. However, due to the limited space and medical resources, deeply deployed WBANs are suffering severe interference problems. The interference affects the reliability and timeliness of data transmissions, and the impacts of interference become more serious in mobile WBANs because of the uncertainty of human movement. In this paper, we analyze the dynamic interference taking human mobility into consideration. The dynamic interference is investigated in different situations for WBANs coexistence. To guarantee the performance of different traffic types, a health critical index is proposed to ensure the transmission privilege of emergency data for intra- and inter-WBANs. Furthermore, the performance of the target WBAN, i.e., normalized throughput and average access delay, under different interference intensity are evaluated using a developed three-dimensional Markov chain model. Extensive numerical results show that the interference generated by mobile neighbor WBANs results in 70% throughput decrease for general medical data and doubles the packet delay experienced by the target WBAN for emergency data compared with single WBAN. The evaluation results greatly benefit the network design and management as well as the interference mitigation protocols design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it