QPRR: QoS-Aware Peering Routing Protocol for Reliability Sensitive Data in Body Area Network Communication
Bibliographic record
Abstract
The reliability, energy efficiency and real-time display of patient's data are important factors for body area network (BAN) communication in indoor hospital environments. In this paper, we propose a novel routing protocol by considering the quality of service (QoS) requirements of BAN data with strict reliability requirements. Our proposed algorithm increases the reliable delivery of critical BAN data at the destination. We have performed extensive simulations in the OMNeT++-based simulator Castalia 3.2 to demonstrate the better performance of the proposed QoS-based routing protocol for reliability sensitive data in terms of lower network routing traffic (Hello packets) overhead, less reliability packets dropped, lower end-to-end delay (latency), less packets dropped due to media access control (MAC) buffer overflow and higher throughput in both stationary and movable patient scenarios. The scalability of the protocol is demonstrated by using two cases that simulate a 24 beds and a 46 beds real hospital environment with a 49 and 93 nodes, respectively. It is shown that, even in the larger real hospital scenarios, simulating a hospital with 24 and 46 beds requiring the transmission of critical data packets with stringent reliability requirements, QPRR outperforms comparable protocols.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".