MétaCan
Menu
Back to cohort
Record W4235806207 · doi:10.32920/ryerson.14644860.v1

Accommodating Machine-To-Machine Traffic In IEEE 802.15.4: The Prioritized Wait Time Approach

2021· preprint· en· W4235806207 on OpenAlex
Vida Azimi

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

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAutomationMachine to machineComputer scienceWirelessComputer networkSmart gridEmbedded systemTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Machine-to-Machine communication (M2M) refers to automated applications executing on smart devices or machines that communicate through a network with little or no human intervention at all. By enabling smart devices to communicate directly with one another, M2M communications technology has the potential to radically change the world around us and the way that we interact with objects. Many applications can benefit from M2M communications, such as transportation, health care, smart energy production, transmission, and distribution, logistics, city automation and manufacturing, security and safety, and others. This work describes an approach to implement M2M communications using the well-known IEEE 802.15.4 / ZigBee communications standard for low data rate wireless personal area networks. In order to achieve better performance for M2M traffic, we propose some improvements in the protocol. Our simulation results confirm the validity

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicIoT Networks and ProtocolsFrench-language works237,207