MétaCan
Menu
Back to cohort
Record W2131694803 · doi:10.1109/icpads.2007.4447732

Prohibition-based MAC protocols for QoS-enhanced mesh networks and high-throughput WLANs

2007· article· en· W2131694803 on OpenAlexaff
Chi‐Hsiang Yeh, Richard Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer networkComputer scienceThroughputQuality of serviceOverhead (engineering)Exponential backoffNetwork packetAsynchronous communicationHidden node problemCollisionChannel (broadcasting)Wireless mesh networkDistributed computingWirelessWireless networkComputer securityWi-Fi arrayTelecommunications

Abstract

fetched live from OpenAlex

To achieve high throughput in wireless networks, collision rate must be very small, while communication overheads (e.g., RTS/CTS dialogues) and channel idleness (e.g., due to backoff) should be both relatively small as compared to data packet durations. Low collision rate is also essential to QoS provisioning in any network employing exponential backoff or a similar strategy. Our proposed solution to the preceding contradicting requirements is to employ prohibition-based mechanisms, which replace the functionality of RTS/CTS dialogues that have been shown to suffer from high communication overhead but only provide limited protection against the hidden terminal problem while in multihop networking environments. The resultant prohibition-based MAC protocols combine binary countdown with busytone, thus inheriting important advantages from both worlds including collision freedom/controllability, prioritization capability, and elimination of hidden terminals. They can also support asynchronous operations which are of practical importance.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.295
Teacher spread0.277 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicWireless Networks and ProtocolsFrench-language works237,207