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Record W2244647934 · doi:10.1109/epec.2015.7379989

Performance evaluation of Channel-Aware MAC protocol in smart grid

2015· article· en· W2244647934 on OpenAlexaff
Abdulfattah Noorwali, Raveendra K. Rao, Abdallah Shami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsWestern University
FundersUmm Al-Qura University
KeywordsComputer scienceNetwork packetComputer networkSmart gridThroughputIEEE 802.11sProtocol (science)Channel (broadcasting)Transmission (telecommunications)Access controlWireless mesh networkReal-time computingWirelessWireless networkEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

An Intelligent Distributed Channel-Aware Medium Access Control (IDCA-MAC) protocol is proposed for Home Area Network (HAN), in general, and for Electrical Devices Networks (EDN), in particular, in smart grids. In an EDN, electrical devices generate critical packets, and these must be communicated to their respective mesh clients. The proposed IDCA-MAC protocol employs simultaneous transmission of data packets through a single collision domain. Also, the electrical devices and the mesh client are assumed to be equipped with Multiple Input Multiple Output (MIMO) technology. The proposed protocol is compatible with the existing IEEE 802.11 standard, and uses channel-aware Medium Access Control (MA-Aware) and the Zig-Zag decoding algorithm. Simulations are carried out using the NS-2 network simulator, and the performance of IDCA-MAC protocol is evaluated for various smart grid scenarios that are of practical significance. It is shown that the protocol is very effective and can be easily adopted in existing smart grids for improved throughput and performance.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.285
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 teacher head, 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

Citations4
Published2015
Admission routes1
Has abstractyes

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