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Record W2155911163 · doi:10.1109/ccece.2009.5090145

Analytical modelling and performance analysis forwireless ad-hoc networks using four-way handshaking mechanism

2009· article· en· W2155911163 on OpenAlexaff
Khalid M. Jamil Khayyat, Fayez Gebali

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHandshakingComputer scienceWireless ad hoc networkMultiple Access with Collision Avoidance for WirelessComputer networkThroughputFrame (networking)Channel (broadcasting)Vehicular ad hoc networkCollisionMobile ad hoc networkTransmission (telecommunications)Flexibility (engineering)WirelessNetwork packetTelecommunicationsComputer security

Abstract

fetched live from OpenAlex

We present a new analytical model of MAC layer for wireless ad hoc networks that takes into account frame retry limits for four-way handshaking mechanism. This model offers flexibility to address key design issues such as the effects of traffic parameters and possible improvements for wireless ad hoc networks. It effectively captures the important network performance characteristics such as throughput, channel utilization, delay and average energy. Under this analytical framework, we evaluate the effect of RTS (request-to-send) state on unsuccessful transmission probability and its effect on the performance specially when the hidden terminal problem is dominant, the traffic is heavy, or the data frame length is very large. By using our proposed model, we show that the probability of collision can be reduced when using RTS/CTS mechanism. Thus, the throughput increases and, at the same time, the delay and the average energy to transmit the frame is reduced.

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 categoriesnone
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 score0.804
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.046
GPT teacher head0.268
Teacher spread0.222 · 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
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

Citations1
Published2009
Admission routes1
Has abstractyes

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