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Record W2172150585 · doi:10.1109/icccn.2008.ecp.80

Gateway Placement in Wireless Mesh Networks Using Free Space Optical Links

2008· article· en· W2172150585 on OpenAlexaff
Mohammed N. Smadi, Sasthi C. Ghosh, Ahmed A. Farid, T.D. Todd, Steve Hranilovic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBackhaul (telecommunications)Computer scienceComputer networkDefault gatewayLinear programmingWirelessInteger programmingFree spaceSoftware deploymentWireless mesh networkCluster analysisWireless networkDistributed computingAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

Wireless mesh networks (WMN) must often be upgraded as usage demands evolve. This is usually done by adding gateways which serve to increase the backhaul capacity of the network. In this paper we study the problem of this type of capacity augmentation using free-space optical (FSO) backhaul links. A joint clustering and gateway placement problem is formulated which includes the strong rate-distance dependence of practical FSO links. The formulation incorporates the positions of existing wireline gateways and minimizes the number of additional hybrid-FSO/RF gateways which are needed to satisfy the new target capacity requirements. A genetic algorithm solution is proposed, and the performance of our algorithm is compared to an optimal solution generated using an integer linear programming (ILP) formulation. Various scenarios are considered which demonstrate the value of using FSO backhaul links to obtain post-deployment capacity upgrades in response to evolving user traffic.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.405
Threshold uncertainty score0.827

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 integrity0.0000.001
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 head0.016
GPT teacher head0.225
Teacher spread0.209 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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