Software defined multihop wireless networks: Promises and challenges
Bibliographic record
Abstract
In multihop wireless networks (MWNs), wireless nodes can communicate with each other through intermediate nodes without the help of any infrastructure. Therefore, wireless nodes are responsible for organizing and configuring the network, and the management of the network is distributed between the nodes. Consequently, it is difficult to overcome the existing challenges such as node mobility and dynamic topology changes, energy constraints, etc. Software defined networking (SDN) is a promising solution, which decouples the control plane and the data plane to overcome the challenges of traditional networks. In the SDN concept, a logically centralized controller makes routing decisions based on the global view of the network and the requirements of applications, and then programs the network. Therefore, it helps to optimize resource allocation and improve the network performance. In this paper, we consider the benefits and the various aspects of applying the SDN concept in MWNs (SDMWN). We first introduce MWNs, existing challenges and the motivation for applying SDN to such networks. Then, after explaining the SDN concept, we review the related work in SDMWN. Finally, we discuss the challenges in applying SDN and future research directions in this area.
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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 | 0.005 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".