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Record W2025231724 · doi:10.1002/wcm.440

Access control in ad hoc networks with selfish nodes

2006· article· en· W2025231724 on OpenAlexafffund
Dongmei Zhao

Bibliographic record

VenueWireless Communications and Mobile Computing · 2006
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer networkWireless ad hoc networkThroughputNetwork packetChannel (broadcasting)Transmission (telecommunications)Transmission delayAccess controlWirelessTelecommunications

Abstract

fetched live from OpenAlex

Abstract We propose an access control scheme for ad hoc networks where nodes compete for transmissions selfishly in order to maximize their own payoffs. A game theory‐based model is first formulated and an equilibrium transmission probability is derived based on the total number of competing nodes, packet transmission error probabilities and other parameters. Packet transmission performance, including both transmission throughput and delay, is then analyzed. Our results show that the proposed channel access scheme allows good spatial reuse of the same channel and its saturation throughput can be much higher than the physical transmission rate of the channel under relatively good channel conditions. The mean packet transmission delay can be kept very low (e.g., less than 10 ms) so long as the offered traffic load is below the capacity. The main advantage of this access control scheme is that it allows fair channel access even nodes are selfish and do not corporate with each other in competing for transmissions. Copyright © 2006 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.249
Teacher spread0.239 · 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

Citations5
Published2006
Admission routes2
Has abstractyes

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