Fairness in delay-aware cross layer data transmission scheme for wireless sensor networks
Bibliographic record
Abstract
Prioritization of the data collected by the Wireless Sensor Networks (WSNs) can provide promising performance enhancements for applications that monitor and control the critical systems. Particularly, in smart grid, defense and e-health applications, delivering high priority data with low latency is important. Previous works have focused on various cross layer approaches for providing Quality of Service (QoS) in WSNs. However, in the ad hoc nature of WSNs it is highly challenging to become aware of selfish nodes that may exploit those QoS-aware approaches. In our previous work, we have proposed a delay-aware cross layer technique for WSNs. In this paper, we propose a cross layer scheme that is both fairness-aware and delay-aware. Our fairness in delay-aware cross layer data transmission scheme (FDRX) is based on delay-estimation and data prioritization steps that are performed before the data transmission by the application layer. If the estimated delay is higher than the acceptable latency range for the high priority data, MAC layer parameters respond to the delay requirements of the application and vary channel access mechanism in a fair manner. Our results show that the proposed FDRX scheme is able to reduce end-to-end delay for data demanding timely delivery while the latency of the other packets is slightly impacted. Furthermore, our approach is able to maintain acceptable performance in terms packet delivery and energy consumption.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".