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Record W1570281671 · doi:10.1109/icc.2015.7248424

Robustness of the routing protocol for low-power and lossy networks (RPL) in smart grid's neighbor-area networks

2015· article· en· W1570281671 on OpenAlexafffund
Quang‐Dung Ho, Yue Gao, Gowdemy Rajalingham, Tho Le‐Ngoc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer networkComputer scienceRobustness (evolution)Routing protocolLossy compressionSmart gridDistributed computingRouting (electronic design automation)Engineering

Abstract

fetched live from OpenAlex

Neighbor-area network (NAN), also known as smart meter communication network, is one of the most important constitutive segments of smart grid communication network. Since almost all smart meters are deployed in hash outdoor environment, they could fail or wireless links between them could be fluctuating over time. These dynamics could hinder the network connectivity and degrade the reliability of data communications. However, the robustness of NANs in the case of network element failures has not received sufficient attention in existing work. This paper therefore proposes a cross-layer scheme that adaptively switches preferred parent nodes in order to help the routing protocol for low-power and lossy networks (RPL), the state-of-the-art implementation of self-organizing routing class, quickly deflect network traffic from points of failures in the NAN scenario. Operation and performance of the proposed scheme in IEEE 802.11-based wireless mesh NANs are studied by simulations.

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)0.000
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 score0.780
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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