Incremental Routing and Scheduling in Wireless Grids
Bibliographic record
Abstract
This paper deals with two fundamental joint routing and scheduling problems in multi-hop wireless mesh networks (WMNs) employing time division multiple access (TDMA). The problems pertain to incremental update of schedules as some of the existing flows terminate and new flow demands are received. In the first problem, referred to as single flow scheduling (SFS) problem, we are given a set of ongoing flows in a WMN, a new incoming flow demand, and a specific potential path for routing the demand. All flows contend for using one of the available wireless channels. We ask whether the new flow demand can be served without perturbing existing slot assignments in the schedule serving the current flows. In the second problem, referred to as single flow routing and scheduling (SFRS) problem, no specific route is given. We first prove that conflict graphs of trees composed of certain class of interference limited paths in wireless networks have bounded treewidth. This characterization yields efficient solution to the SFS problem, among a number of other resource allocation problems in wireless networking. Next we consider the SFRS problem in grid networks. For such networks, we present an efficient solution to a generalized version of the SFRS problem where each link is associated with a cost, and a minimum cost schedulable route is desired. Using both concrete examples and simulation, we show that the devised SFRS algorithm yields improved throughput results over a competing approach that uses tree based routing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".