A Comparative Analysis between Centralized Routing and Distributed Routing in Multi-Hop Wireless Networks
Bibliographic record
Abstract
A growing desire for granular network control, automation, virtualization and much more, has contributed to the emergence of Software Defined Networking (SDN) as a prominent research area.Though originally developed for wired networks, benefits of the centralized routing approach are now being leveraged for wireless network applications.These include SDN-based Multi-hop Wireless Networks (MWNs), as potential alternatives to traditional MWNs.This thesis presents a Software Defined Multi-hop Wireless Network (SDMWN) solution, with standard centralized routing characteristics and full mobility capabilities, evaluated against distributed routing in an equivalent traditional MWN architecture.Our emulation results, obtained with Mininet-WiFi, demonstrate a good degree of potential for SDMWN when operating under controlled (mobile) network conditions.By guaranteeing the availability of potential links between every node, SDMWN outperforms the traditional MWN, by about 15% and 65 ms, for Ping Success Rate and Round-Trip Time respectively.However, this comes at a relatively high cost of overhead.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".