Energy Aware Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
Bibliographic record
Abstract
Cognitive radio networks (CRNs) emerged as a paradigm to solve the problem of limited spectrum availability and the spectrum underutilization in wireless networks by opportunistically exploiting portions of the spectrum temporarily vacated by licensed primary users (PUs). Routing in CRNs is a challenging problem due to the PU activities and mobility. On the other hand, energy aware routing is very important in energy-constraint CRNs. In addition, it is crucial that CR users efficiently exchange data with each other before the appearance of PUs. To design a robust routing scheme for mobile CR ad hoc networks (CRANs), the constraints on residual energy of each CR user, reliability, and the protection of PUs must additionally be taken into account. Moreover, multipath routing has great potential for improving the end-to-end performance of ad hoc networks. Considering all these evidences, in this paper, we propose an energy aware on-demand multipath routing (EOMR) protocol for mobile CRANs to ensure the robustness and to improve the throughput. The proposed routing scheme involves energy efficient multipath route selection and spectrum allocation jointly. The simulation results show that our approach improves the overall performance of the network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".