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Record W1974608850 · doi:10.1109/mcom.2014.6807949

A survey of access management techniques in machine type communications

2014· article· en· W1974608850 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Communications Magazine · 2014
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMachine to machineAutomationControl communicationsQuality of serviceCommunications systemScheme (mathematics)TelecommunicationsComputer networkComputer securityInternet of Things

Abstract

fetched live from OpenAlex

Machine-to-machine communications can be defined as ubiquitous communications among machines to perform diversified activities such as sensing, processing, decision making, and acting on decisions. The main trait that differentiates M2M from other variations of communications is the lack of human supervision in the communications lifecycle. However, increased automation resulted in many heterogeneous novel applications taking advantage of M2M. This eventually caused an explosion in the number of devices participating in M2M. Therefore, managing the massive number of accesses to satisfy the QoS requirements of the different applications running on those devices has become an issue of great concern. In this article, we survey the existing access management approaches for M2M communication, aiming at a novel classification scheme that will serve as a guide and motivation toward further research in this area.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.520

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 communication0.0000.000
Open science0.0020.001
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 head0.073
GPT teacher head0.343
Teacher spread0.270 · 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