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Record W2160417792 · doi:10.1109/icc.2007.632

Shared Infrastructure Power Saving for Solar Powered IEEE 802.11 WLAN Mesh Networks

2007· article· en· W2160417792 on OpenAlexaff
Edgar Vargas, Amir A. Sayegh, T.D. Todd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceSoftware deploymentBandwidth (computing)Computer networkPower consumptionIEEE 802.11Mesh networkingPower (physics)TelecommunicationsWirelessWireless lan

Abstract

fetched live from OpenAlex

Solar powered WLAN infrastructure is a cost effective option in outdoor deployments where continuous power sources are not practical. In these nodes the cost of the solar panel and battery can be a significant fraction of the total, and therefore reducing access point power consumption is very important. In this type of network, peak bandwidth requirements may not be satisfied by a single access point radio, even though long term average bandwidth requirements may be very low. In this case multiple radio APs or overlapped AP coverage deployment is required to meet this peak demand. When this happens the long term power consumption of the nodes can be reduced by implementing shared dynamic power saving between the WLAN mesh nodes. In this paper we propose and evaluate two algorithms for efficiently activating the solar powered infrastructure when additional bandwidth is needed. The algorithms are designed to be compatible with the existing IEEE 802.11 standard and include conventional load balancing when more than one AP is active in a given coverage area. We demonstrate that the proposed algorithms can significantly reduce the power consumption of the shared solar powered infrastructure.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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
GenreMethods

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
Published2007
Admission routes1
Has abstractyes

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