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Record W2047892990 · doi:10.1049/iet-ifs.2009.0197

Physical layer assisted authentication for distributed <i>ad hoc</i> wireless sensor networks

2010· article· en· W2047892990 on OpenAlexafffund
Haiying Wen, Pin-Han Ho, Chun Qi, Guang Gong

Bibliographic record

VenueIET Information Security · 2010
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Electronic Science and Technology of China
KeywordsComputer networkComputer scienceAuthentication (law)Authentication protocolPhysical layerOverhead (engineering)Message authentication codeWireless ad hoc networkPublic-key cryptographyCryptographyDistributed computingWirelessComputer securityEncryptionTelecommunications

Abstract

fetched live from OpenAlex

The paper introduces a novel message authentication framework over broadcast channels, where a symmetric cryptography-based physical layer assisted message authentication (PLAA) scheme is introduced in wireless networks. The proposed framework integrate the conventional message authentication schemes and the physical layer authentication mechanisms by taking advantage of temporal and spatial uniqueness in physical layer channel responses, aiming to achieving fast authentication while minimising the packet transmission overhead. Our claims through extensive analysis and simulation will be verified via comparing with public key infrastructure-based PLAA scheme and traditional upper layer authentication schemes.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations39
Published2010
Admission routes2
Has abstractyes

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