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Record W2889409284 · doi:10.1109/iwcmc.2018.8450523

Energy Efficiency Analysis of Centralized-Synchronous LoRa-based MAC Protocols

2018· article· en· W2889409284 on OpenAlexaff
Galal Hassan, Mohamed ElMaradny, Mohamed Ibrahim, Abdulmonem M. Rashwan, Hossam S. Hassanein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceComputer networkEfficient energy useEmbedded systemElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

LoRa is a PHY layer technology that has been gaining popularity with IoT platfrom developers, due to its low-power long-range communication. As a result, different classes of LoRabased MAC layer protocols have been proposed, with the key ones being either contention-based or centralized-synchronous. Since most of the research literature focused on analyzing the efficiency of contention-based LoRa protocols, we sought to study the efficiency of centralized-synchronous protocols. We utilized a tailored simulator to analyze the energy efficiency of LoRa-based centralized-synchronous protocols. Our findings, are backed up by hardware performance measurements. After comparing the energy efficiency of the centralized-synchronous protocols against that of other LoRa-based MAC layer protocol classes, we found that the lifetime of a device using a centralized-synchronous protocol was up to four times longer than that of a contentionbased device. These findings, as well as our insights, will aid the development of future energy-efficient LoRa-based MAC protocols.

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)0.002
metaresearch head score (Gemma)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.261
Teacher spread0.250 · 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
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

Citations14
Published2018
Admission routes1
Has abstractyes

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