Preemptive Multicast Routing in Mobile Ad-hoc Networks
Bibliographic record
Abstract
Preemptive route maintenance allows a routing algorithm to maintain connectivity by preemptively switching to a path of higher quality when the quality of the currently used path is deemed questionable. Preemptive routing initiates recovery actions early by detecting that a link is likely to be broken soon and searching for a new path before the current path actually breaks. Preemptive route maintenance has been used for unicast (point-to-point) communications in wired networks and in mobile ad-hoc networks (MANETs) to minimize the number of route breaks and thus packet losses, and end-to-end delays. In addition to these advantages, we show that preemptive route maintenance can help minimize control overhead and improve the scalability of multicast routing protocols in MANETs. In this paper, we present design and implementation issues of preemptive routing for multicast in MANETs. We then describe a preemptive multicast routing protocol based on ODMRP (On-Demand Multicast Routing Protocol), which we call PMR (Preemptive Multicast Routing). PMR significantly improves the scalability of ODMRP: it offers similar or higher packet delivery ratios while incurring much less control overhead. Our simulation results have confirmed these advantages of PMR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".