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Wireless Meter Bus: Secure Remote Metering within the IoT Smart Grid

2022· article· en· W4291803904 on OpenAlexaff
Wafaa Anani, Abdelkader Ouda

Bibliographic record

Venue2022 International Symposium on Networks, Computers and Communications (ISNCC) · 2022
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsWestern University
Fundersnot available
KeywordsMetering modeInternet of ThingsComputer scienceMetreWirelessSmart meterAutomatic meter readingSmart gridEmbedded systemComputer networkTelecommunicationsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Smart metering and wireless communication technologies offer immense possibilities for scalable solutions to automated meter reading and energy production and distribution networks (smart grid). Around the world, researchers are working on methods for securing smart grid operations in general and smart metering in particular. Yet, their main focus remains on implementing and evaluating wireless communication technologies as an adequate solution to the smart grid. This paper proposes a security framework within the smart grid communication network. A new security profile called ‘W’ profile is introduced to cover all aspects of security protocols for Wireless M-Bus. Specifically, it aims to secure automated meter reading (AMR) using the Wireless M-Bus protocol within the Open Metering System (OMS) security standards in terms of authentication, integrity, and confidentiality.

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: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.798

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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