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Record W3134032143 · doi:10.1109/mce.2021.3062800

Consumer Electronic Devices: Evolution and Edge Security Solutions

2021· article· en· W3134032143 on OpenAlexaff
Shalli Rani, Divya Gupta, Sahil Garg, Md. Jalil Piran, M. Shamim Hossain

Bibliographic record

VenueIEEE Consumer Electronics Magazine · 2021
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceComputer securityMobile deviceEnhanced Data Rates for GSM EvolutionAuthentication (law)The InternetEdge deviceVariety (cybernetics)TelecommunicationsWorld Wide WebCloud computing

Abstract

fetched live from OpenAlex

The pervasive growth in the variety of consumer electronic devices (CEDs), as well as the Internet traffic, has evolved various security risks toward their usage. This article presents the concept of communications without a centralized server to facilitate the fastest communication between end-devices, e.g., user handheld devices, and edge devices. Three communication scenarios are presented to support authentication in the proposed architecture. To understand the concept, this article first describes the growth in the CEDs market. Afterward, the discussion on various challenges faced for incorporating security in such devices is presented along with edge-based security solutions. Finally, the article presents a use-case of the content delivery network to discuss various security solutions based on the edge with the hope that these solutions will work for future security.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.006
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2021
Admission routes1
Has abstractyes

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