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Record W1536563834 · doi:10.1109/ccece.2005.1557394

Mobile ad-hoc networks with QoS and RSVP provisioning

2006· article· en· W1536563834 on OpenAlexaff
Sasan Adibi, S. Erfani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer networkComputer scienceQuality of serviceMultiprotocol Label SwitchingIntegrated servicesScalabilityResource Reservation ProtocolMobile ad hoc networkDifferentiated servicesMobile QoSDistributed computingService (business)Service providerThe InternetInternet ProtocolNetwork packet

Abstract

fetched live from OpenAlex

This paper we discuss the architecture of a QoS based mobile ad-hoc network MANET using RSVP over multiprotocol label switch (MPLS). Classical IP routing provides only a "best effort" service, which makes routing simple, however no quality of service (QoS) will be provided to applications such as streaming voice and video. For the purpose of scalability, where many connections are to be connected and treated according to the defined class for backbone networks, differentiated services (DiffServ) is used. In order for our system architecture to support per-flow QoS, resource-reservation protocol (RSVP) and DiffServ have to work hand-in-hand with RSVP. However in general, DiffServ routers do not understand RSVP messages. Independent DiffServ domains may use different IntServ-to-DiffServ mappings. We consider this mapping and the ultimate goal of this paper is to use DiffServ-IntServ-RSVP-MPLS mapping to enhance the operation in the MANETs and we'll reflect the simulation results comparing MANETs with QoS-enabled to MANETs with no QoS option provided

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)0.000
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 score0.817
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.192
Teacher spread0.188 · 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

Citations10
Published2006
Admission routes1
Has abstractyes

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