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Record W2170507502 · doi:10.1109/lcn.2003.1243205

Reliable multipath routing with fixed delays in manet using regenerating nodes

2004· article· en· W2170507502 on OpenAlexaff
Rui Ma, Jacek Ilow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceComputer networkNetwork packetMobile ad hoc networkMultipath routingRedundancy (engineering)End-to-end delayNode (physics)Wireless ad hoc networkMultipath propagationNetwork topologyDistributed computingRouting protocolOptimized Link State Routing ProtocolPacket lossLink-state routing protocolWirelessChannel (broadcasting)Telecommunications

Abstract

fetched live from OpenAlex

This paper proposes a new framework in mobile ad hoc networks (MANET) for reliable multipath routing with fixed delays based on packet level forward error control (FEC). The novelty of this work stems from the integrated optimization of the redundancy at the path and the FEC packet levels to arrive at the concept of the regenerating nodes. The regenerating nodes can reduce the packet loss rate (PLR) between the source and the intermediate nodes so that, eventually, the PLR between the source and the destination is minimized. In general, the residual PLR in the system is reduced to the PLR on the connection between the last regenerating node and the destination. Extensive Monte Carlo simulations are provided to demonstrate the robust performance of the proposed scheme in the network environments with frequently changing topologies and PLR scenarios. The scheme accommodates various constraints for delay and reliability in terms of PLR that can be tailored to the specific applications, such as real-time multimedia services in MANET.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.207
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations22
Published2004
Admission routes1
Has abstractyes

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