A novel framework for cross-layer design in wireless ad hoc and sensor networks
Bibliographic record
Abstract
In a wireless ad hoc or sensor network, the behavior of any protocol entity and the operations carried out by such entities impact the performance. Separation between the different layers of the OSI model has been a common practice. Nevertheless, it is the unpredictability and unreliability of the underlying wireless medium, the complexity of contention-based medium access, and the dynamic nature of wireless multi-hop networks that account for our anticipation of resorting to cross-layer interactions between the network, upper, and lower layers (application, transport, MAC and PHY) to achieve energy efficiency, reliable packet delivery, and stability, in multi-hop wireless networks. We herein propose a novel framework, namely dynamic multi-attribute cross-layer design (DMA-CLD), in which multiple, and possibly conflicting, (single-layer, cross-layer, nodal, and networking) objectives are met. To our best knowledge, this is the first time that such a multi-objective framework is proposed. More importantly, our framework can be easily extended to accommodate any number of objectives and OSI layers, provided that the proper inter-layer feedback is integrated into DMA-CLD. We study some of the key characteristics of the proposed framework and show that DMA-CLD is an efficient and computationally inexpensive mechanism via which adhering to a predefined, precedence-ordered set of objectives is possible.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".