High-performance multicast in multi-channel multi-radio wireless mesh networks
Bibliographic record
Abstract
Wireless mesh networking (WMN) is an emerging technology that enables multi-hop wireless connectivity to areas where wiring or installing cables is difficult or expensive. Multicast is a form of communication that delivers information from a source to a group of destinations simultaneously in an efficient manner. In a single-channel WMN, all nodes share and communicate with each other via the same channel. In such a network, the throughput capacity of multicast degrades significantly as the network size increases. A critical factor that contributes to this rapid degradation is the co-channel interference in single-channel WMNs, worsened by the use of single, half-duplex radio per node. A node with a single half-duplex radio is restricted to access one channel at a time, and thus cannot transmit and receive simultaneously. One of the most effective approaches to achieve high throughput is to use systems with multiple channels and multiple radios (MCMR) per node. An MCMR node may transmit on one channel and receive on another at the same time using two different radios, and thus at least double the throughput. In this thesis, we propose solutions to support high-performance multicast in MCMR WMNs, as follows. 1. We propose a novel channel assignment (CA) algorithm for multicast that minimizes interference among forwarding nodes, because existing CA algorithms for multicast suffer very low performance due to lack of interference-free solutions. 2. We propose routing algorithms that outperform traditional multicast routing schemes by taking into account the wireless broadcast advantage (WBA) and the underlying CA in order to minimize network bandwidth consumption. Traditional multicast routing algorithms such as shortest path tree and Steiner tree did not consider the WBA or the underlying CA in MCMR WMNs. 3. We develop analytical models for estimating the performance of network-coded multicast in MCMR WMNs, and validate the proposed models using realistic simulation-based scenarios. Network coding has been proven to be a promising technique for improving network throughput of WMNs. However, the performance of a multicast session in combination with network coding in the MCMR environment has not been studied prior to this research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".