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Record W2089320128 · doi:10.1109/iccct.2014.7001503

A robust hybrid-MAC protocol for M2M communications

2014· article· en· W2089320128 on OpenAlexaff
Pawan Kumar Verma, Rajeev Tripathi, Kshirasagar Naik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTime division multiple accessComputer networkComputer scienceFrame (networking)AlohaScalabilityThroughputChannel (broadcasting)Synchronization (alternating current)ReservationTransmission (telecommunications)WirelessTelecommunications

Abstract

fetched live from OpenAlex

In M2M communications, there is a critical requirement of a robust MAC protocol to enable multiple M2M devices to access the channel. For this purpose, reservation or contention based MAC protocols can be used, but with multiple M2M devices, adaptability, and scalability become bottlenecks. Therefore, a frame based hybrid-MAC scheme, consisting of a CSMA-based contention period, and a TDMA-based transmission period has been proposed here. During contention period, the devices compete for the channel access. The successful devices during this period will strive to transmit data, using IEEE 802.11 DCF mechanism within each TDMA slot. The aim is to make sure that in case of TDMA clock synchronization failure, there is no communication failure between M2M devices. Extensive simulation results in ns-2 environment show that the proposed time frame scheme for hybrid-MAC protocol performs better than slotted-ALOHA and TDMA in terms of aggregate throughput and average end-to-end delay.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.071
GPT teacher head0.310
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2014
Admission routes1
Has abstractyes

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