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Record W2031088461 · doi:10.1109/glocom.2009.5425638

Modeling the Throughput and Delay in Wireless Multihop Ad Hoc Networks

2009· article· en· W2031088461 on OpenAlexafffund
Ahmad Ali Abdullah, Fayez Gebali, Lin Cai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWireless ad hoc networkComputer networkComputer scienceAd hoc wireless distribution serviceMultiple Access with Collision Avoidance for WirelessMobile ad hoc networkWireless networkWirelessThroughputOptimized Link State Routing ProtocolVehicular ad hoc networkStochastic geometry models of wireless networksDistributed computingNetwork packetTelecommunications

Abstract

fetched live from OpenAlex

In wireless multihop ad hoc networks, the use of the RTS/CTS mechanism does not completely eliminate the hidden-terminal problem. Considering the hidden-terminal problem adds complexity to the existing analysis for single-hop networks. In this paper, we provide precise and accurate analytical models for quantifying the throughput and delay in wireless multihop ad hoc networks. The proposed analysis is applicable to many wireless MAC protocols and applications. The accuracy of our analytical models are verified by extensive NS-2 simulations. Our analysis reveals how the throughput and delay in wireless multihop ad hoc networks are affected by the hidden-terminals and by the transmission and interference ranges of wireless devices. These results are important for network planning and protocol optimization in wireless multihop ad hoc networks.

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)0.008
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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

Citations25
Published2009
Admission routes2
Has abstractyes

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