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Record W2110203913 · doi:10.1109/ccnc.2006.1593060

OSPF-MCDS-MC: a routing protocol for multi-channel wireless ad-hoc networks

2006· article· en· W2110203913 on OpenAlexaff
Unghee Lee, Scott F. Midkiff, Tao Lin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsMcMaster University
FundersOffice of Naval Research
KeywordsComputer networkComputer scienceWireless Routing ProtocolRouting protocolZone Routing ProtocolDistributed computingDynamic Source RoutingGoodputOptimized Link State Routing ProtocolLink-state routing protocolEnhanced Interior Gateway Routing ProtocolWireless ad hoc networkOpen Shortest Path FirstInterior gateway protocolHazy Sighted Link State Routing ProtocolRouting (electronic design automation)WirelessThroughputTelecommunications

Abstract

fetched live from OpenAlex

We present an innovative routing protocol that utilizes multiple channels to improve performance in a mobile ad hoc network. The basic idea of the design is to provide nodes with the ability to use multiple channels effectively so that multiple useful transmissions can occur simultaneously, thus improving network capacity. The proposed scheme requires minor changes to existing proactive or table-driven routing protocols and no modifications to current the IEEE 802.11 medium access control protocol. To avoid inefficiencies due to transmission of periodic updates in proactive routing protocols, the proposed scheme divides the network layer into control and data planes. To demonstrate the multi-channel routing scheme, we extend the OSPF-MCDS routing protocol to a multi-channel version, OSPF- MCDS-MC. Simulation results indicate that OSPF-MCDS-MC successfully exploits multiple channels to improve network capacity. The routing protocol allows the network goodput to increase in proportion to the number of available channels, even as the number of nodes and network load increase, in both single- hop and multiple-hop 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 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 categoriesMeta-epidemiology (narrow)
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 score0.645
Threshold uncertainty score1.000

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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.

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

Citations5
Published2006
Admission routes1
Has abstractyes

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