Network Coding and Quality of Service for Mobile <i>Ad Hoc</i> Networks
Bibliographic record
Abstract
Network Coding is a relatively new forwarding paradigm where intermediate nodes perform a store, code, and forward operation on incoming packets. Traditional forwarding approaches, which employed a store and forward operation, have not been able to approach the limit of the max-flow min-cut throughput wherein sources transmitting information over bottleneck links have to compete for access to these links. With Network Coding, multiple sources are now able to transmit packets over bottleneck links simultaneously, achieving the max-flow min-cut through-put and increasing network capacity. While the majority of the contemporary literature has focused on the performance of Network Coding from a capacity perspective, the aim of this research has taken a new direction focusing on two Quality of Service metrics, e.g., Packet Delivery Ratio (PDR) and Latency, in conjunction with Network Coding protocols in Mobile Ad Hoc Networks (MANETs). Simulations are performed on static and mobile environments to determine a Quality of Service baseline comparison between Network Coding protocols and traditional ad hoc routing protocols. The results show that the Random Linear Network Coding protocol has the lowest Latency and Dynamic Source Routing protocol has the highest PDR in the static scenarios, and show that the Random Linear Network Coding protocol has the best cumulative performance for both PDR and Latency in the mobile scenarios.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".