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Record W2155785578 · doi:10.1109/tmc.2006.130

End-to-End Batch Transmission in a Multihop and Multirate Wireless Network: Latency, Reliability, and Throughput Analysis

2006· article· en· W2155785578 on OpenAlexafffund
Teerawat Issariyakul, Ekram Hossain, Attahiru Sule Alfa

Bibliographic record

VenueIEEE Transactions on Mobile Computing · 2006
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
FundersAsian Institute of TechnologyThammasat UniversityUniversity of British ColumbiaUniversity of Manitoba
KeywordsComputer scienceComputer networkNetwork packetLatency (audio)Hybrid automatic repeat requestWireless networkAutomatic repeat requestWirelessTransmission (telecommunications)Transmission Control ProtocolSelective Repeat ARQNode (physics)Reliability (semiconductor)End-to-end principleMarkov chainTelecommunicationsEngineeringTelecommunications link

Abstract

fetched live from OpenAlex

This paper presents a novel Markov-based model for analyzing the end-to-end transmission of a batch of packets in a multihop wireless network using multirate transmission. The end-to-end reliability of this transmission (in terms of the number of packets delivered to the destination node) is controlled through different types of automatic repeat request (ARQ)-based error control mechanisms implemented at each node. For a batch of packets, we derive complete statistics (i.e., probability mass function) for end-to-end latency and the number of packets successfully delivered to the destination node. Typical numerical results obtained from the model are validated by means of simulation. These results reveal the trade-off between end-to-end latency and end-to-end reliability, which would be an important issue in designing and engineering multihop wireless networks. Also, we demonstrate the usefulness of the proposed analytical model in predicting the latency and the reliability performances of TCP (transmission control protocol) in a multihop wireless scenario

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.577
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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
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

Citations13
Published2006
Admission routes2
Has abstractyes

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