MétaCan
Menu
Back to cohort
Record W2137095935 · doi:10.1109/lcn.2002.1181868

Infrastructure-based MAC in wireless mobile ad-hoc networks

2003· article· en· W2137095935 on OpenAlexaff
Tiantong You, Hossam S. Hassanein, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer networkComputer scienceNetwork allocation vectorMultiple Access with Collision Avoidance for WirelessWireless ad hoc networkInter-Access Point ProtocolIEEE 802.11Network packetDistributed coordination functionService setBackward compatibilityIEEE 802.11sAd hoc wireless distribution serviceVehicular ad hoc networkMedia access controlWireless networkMobile ad hoc networkWirelessOptimized Link State Routing ProtocolWi-FiRouting protocolTelecommunicationsWireless mesh network

Abstract

fetched live from OpenAlex

In this paper, an efficient and on-the fly infrastructure is created using our proposed mobile point coordinator (MPC) protocol. Based on this protocol, we also develop an efficient MAC protocol, namely MPC-MAC. Our MAC protocol extends the IEEE 802.11 standard for use in multihop wireless ad-hoc networks implementing both the DCF and PCF modes of operation. The goal, and also the challenge, is to achieve QoS delivery and priority access for real-time traffic in ad hoc wireless environments while maintaining backward compatibility with the IEEE 802.11 standard. The performance of MPC-MAC is compared to the IEEE 802.11 DCF-based MAC without MPC. Simulation experiments show that in all cases the use of PCF benefits real-time packets by decreasing the average delay and the discard ratio. However, this may come at the expense of increasing the average delay for non-real-time data. On the other hand, the discard ratio for both real-time and non-real-time packets improves with the use of PCF. Therefore, our MPC-MAC outperforms the standard DCF IEEE 802.11 MAC protocol in multi-hop ad-hoc environments.

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: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.879

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 communication0.0000.000
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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

Citations19
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicMobile Ad Hoc NetworksFrench-language works237,207