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Record W2147136121 · doi:10.1109/glocom.2009.5425699

Incremental Routing and Scheduling in Wireless Grids

2009· article· en· W2147136121 on OpenAlexafffund
Abdullah-Al Mahmood, Ehab S. Elmallah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceScheduling (production processes)Flow routingTime division multiple accessDistributed computingComputer networkWireless mesh networkGridJob shop schedulingScheduleWirelessRouting (electronic design automation)Mathematical optimizationWireless networkEngineeringMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.235
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2009
Admission routes2
Has abstractyes

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