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Record W2165656946 · doi:10.1109/wcnc.2008.402

Optimal Node Placement in Hybrid Solar Powered WLAN Mesh Networks

2008· article· en· W2165656946 on OpenAlexaff
Amir A. Sayegh, Sasthi C. Ghosh, T.D. Todd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceMathematical optimizationNode (physics)ProvisioningMesh networkingInteger programmingUpper and lower boundsTopology (electrical circuits)Computer networkAlgorithmMathematicsWirelessEngineeringTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

Hybrid WLAN mesh networks use a combination of nodes that are continuously powered and those that are powered using an energy sustainable source such as solar power. In this paper we consider the problem of cost-optimal placement of the energy sustainable nodes in these types of hybrid networks. We first introduce a cost model that takes into account the provisioning required to operate the solar/wind powered nodes subject to a desired node outage criterion. We then formulate the design problem as a Mixed Integer Quadratic Problem (MIQP). A branch and bound approach is used to obtain node positioning solutions and is compared with a proposed algorithm that uses optimum shortest path routes. Our results show that there is a significant improvement in cost that can be obtained using the proposed methodology and that the branch and bound approach achieves the optimum assignment for a variety of network examples.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.195
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes1
Has abstractyes

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