Routing Metrics for Minimizing End-to-End Delay in Multiradio Multichannel Wireless Networks
Bibliographic record
Abstract
This paper studies how to select a path with the minimum expected end-to-end delay (EED) in a multiradio multichannel (MR-MC) wireless mesh network. While the existing studies mainly focus on the packet transmission delay due to medium access control (MAC), our new EED metric further takes into account the queuing delay at the MAC layer. In particular, in the MR-MC context, we develop a generic iterative approach to compute the multiradio achievable bandwidth (MRAB) for a path, taking the impact of inter-/intraflow interference and space/channel diversity into consideration. The MRAB is then combined with the EED to form the metric weighted end-to-end delay (WEED). As a byproduct of MRAB, a channel diversity coefficient is defined to quantitatively represent the channel diversity for a given path. Moreover, we design and implement a distributed WEED-based routing protocol for MR-MC wireless networks by extending the well-known AODV protocol. Extensive simulation results are presented to demonstrate the performance of EED/WEED-based routing, with comparison to some existing well-known routing metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".