An Algorithm for Incremental Joint Routing and Scheduling in Wireless Mesh Networks
Bibliographic record
Abstract
In this paper we explore a fundamental joint routing and scheduling problem in wireless mesh networks (WMNs) that employ time division multiple access (TDMA). The problem, referred to as the minimum cost single flow routing and scheduling (MC-SFRS) problem, deals with incremental update of transmission schedules necessitated by dynamic arrival of new flows and termination of existing flows during the operation of the network. In the problem, we are given a multi-hop WMN, a set of ongoing flows, a transmission schedule for the ongoing flows, a set of costs associated with links, and a new flow demand. All flows contend for using one of the available wireless channels. The problem asks for finding a non-bifurcated route with minimum cost along which the new flow can be scheduled without perturbing slot assignments in the given schedule, if such route exists. Our main contribution is an efficient algorithm for solving the MC-SFRS problem for arbitrary interference relations among pairs of transmission links in networks with arbitrary topologies. Among other classes of routes, our algorithm is exact over the class of shortest routes. The obtained simulation results demonstrate the effectiveness of our proposed algorithm over the competing method of exact scheduling for a fixed routing tree. In addition, the results show improvement obtained by using our algorithm to augment the schedules obtained by fixed tree 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".