Performance Analysis of Delay Distribution and Packet Loss Ratio for Body-to-Body Networks
Bibliographic record
Abstract
With the increasing wide applications of wearable wireless networks, body-to-body networks (BBNs) have become significantly important to provide timely and reliable data delivery services. For a specific BBN, assessing its theoretically achievable Quality of Service (QoS) is necessary, especially on the key performance metrics of end-to-end delay distribution and packet loss ratio. The existing analysis models in the literature mainly focused on 1-D space scenarios. In this article, BBN in a 2-D area is considered, where mobile nodes freely and stochastically move along lanes. By introducing two new definitions: 1) node entrance probability and 2) network entrance probability, a systematically analytical framework for end-to-end delay distribution and packet loss ratio is presented. The proposed analytical framework is built on three critical techniques: 1) the Markov chain to model node behaviors; 2) the first passage theory to calculate node entrance probability and network entrance probability; and 3) the central limit theory to decrease the computation time for summing up per-hop delay. Simulation results demonstrate the effectiveness and accuracy of our proposed analysis model.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".