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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".