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Record W2122761214 · doi:10.1002/ett.2936

Congestion and overload control techniques in massive M2M systems: a survey

2015· article· en· W2122761214 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTransactions on Emerging Telecommunications Technologies · 2015
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceComputer networkCloud computingNetwork congestionCellular networkInternet of ThingsThe InternetMachine to machineControl (management)Computer securityWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

Abstract Smart applications, Internet of things, cloud computing technologies are becoming connected together by autonomous and intelligent devices. Communication among those devices, called machine‐type communication (MTC) can be done through cellular networks. A cellular network typically handles both voice and data traffic. Cellular based machine‐to‐machine (M2M) communication utilises cellular infrastructure to convey their information among MTC devices. Although MTC devices generate small amount of data traffic, massive amount of MTC devices create overload problem which hugely impacts the radio access and core networks of the cellular system. This article discusses M2M systems, its various applications, architectural requirements, functionalities, services and features from congestion and overload control point of view. In particular, a survey of congestion control problem in M2M systems and its various solutions are discussed in this article. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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 score0.942
Threshold uncertainty score0.735

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 science0.0000.000
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 head0.030
GPT teacher head0.273
Teacher spread0.243 · 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