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Record W2147094743 · doi:10.1109/ccece.2005.1557166

Afast handover m-manet with QoS support

2006· article· en· W2147094743 on OpenAlexaff
Sasan Adibi, Mohammad Naserian, S. Erfani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer networkMobile IPComputer scienceIPv4Multiprotocol Label SwitchingMobile ad hoc networkHandoverQuality of serviceIPv6IP address managementNode (physics)Optical IP SwitchingMobile computingTriangular routingNetwork packetInternet ProtocolRouting protocolThe InternetOptimized Link State Routing ProtocolEngineering

Abstract

fetched live from OpenAlex

Looking at the progress of mobile-IP in the recent years, there's a sense that IP (for QoS support, IPv6 more specifically) is going to be involved more and more in wireless applications. The current IETF standard for mobility is the mobile IP (RFC 3344 for IPv4 and RFC 3775 for IPv6) both work by changing the IP address when changing the subnet. Both mobile IPv4 and IPv6 suffer from longer handover delays mainly due to AAA (authentication, authorization and accounting) signalling and IP address configuration. There are numerous proposals out there, which try to either optimize mobile IP or use different mechanisms for a certain domain. What proposed here is the deployment of existing technologies binding to a new approach in handling QoS and smooth and technology independent handoffs, thanks to the MPLS mechanism and to the adaptive characteristics of mobile ad-hoc networks (MANETs). This paper discusses a mechanism for a fast handoff in mobile-IPv6 architecture. Fast handoff in mobile-IP is used for facilitating applications such as video-conferencing, Internet telephony, and other applications that require minimal delays and packet drops. In our proposal, multipath routing approach facilitates the communication between M-MANET entities "mobile node (MN), correspondent nodes (CN), and home agent (HA)". These entities are all IPv6-ad-hoc-MPLS-ready elements and this is an MPLS mobile-IPv6 ad-hoc network (M-MANET) topology, therefore QoS will be maintained through the usage of this integration. With simulation results we discuss the overall functionality

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.999

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)0.0020.000

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 head0.002
GPT teacher head0.147
Teacher spread0.145 · 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 designNot applicable
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

Citations2
Published2006
Admission routes1
Has abstractyes

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